Author: nwjunker

Using model guidance to forecast QPF

1)

Heavy precipitation forecasting, using numerical models.

2)

Uncertainty in the initial conditions.

3)

Ensembles, an introduction.

A)

Using climatological anomalies to predict extreme rainfall events

B)

An example of how you might use anomalies to make a forecast.

C)

Another case...using ensemble guidance and standardized anomalies.

4)

GFS Model

5)

Terrain and resolution issues

6)

The NAM

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Heavy precipitation forecasting, using numerical models.

There has been a steady improvement in the accuracy of HPC quantitative precipitation forecasts since the 1960s. Much of that improvement is directly attributable increases in the vertical and horizontal resolution, improvements in data assimilation and model physics which have led to better mass and wind field forecasts and improved forecasts of quantitative precipitation. Despite these improvements, HPC forecasters have been able to improve model forecasts.

The discussion that follows will be an attempt to provide forecasters with tools to improve and modify the models’ QPF. Before attempting to make precipitation forecast, it is important to understand why models sometimes have problems during a forecast and to understand some of their biases. The sections that follow with attempt to explain: 1) why there is uncertainty in any model forecasts, 2) briefly discuss the operational NCEP models and their recent performance characteristics (as of winter 2007), 3) briefly discuss ensemble forecasting, 4) offer examples of how ensemble guidance can be used to predict extreme rainfall events.

Why models have forecasting Problems

Despite significant increases in computing power and increase resolution of operational models, improvements in the accuracy of quantitative precipitation forecasts has been slow. There are a number of factors that can contribute to operation models producing forecast errors.
there may not be enough data to resolve features. This is especially true over data sparse areas and the oceans. Moisture and temperature derived from satellite data may not have the desired vertical resolution.
Some physical processes operate at a scale below that of the resolution of the models so they need to be parameterized. Essentially, physical representations of the effects of these processes need to developed. Radiation, convection, boundary layer processes, evapo-transpiration and within cloud microphysical are some of the processes that are currently parameterized in the operational models. Any shortcoming of a parameterization scheme can adversely impact the forecast which may lead to a poor precipitation forecast. However, one parameterization scheme can impact on another.
Atmospheric processes are non-linear meaning that small differences in the initial condition can lead to significant differences in a forecast. Also, two models with different convective schemes may produce different precipitation forecasts and the latest heat release from the condensation may feed back into the mass and wind field forecasts with time. The non-linear response to atmospheric forcing and the chaos it engenders is the reason for the development of ensemble forecast systems.
The current generation of operation models may not have enough resolution to resolve some features.

No model not matter what the resolution will have a perfect deterministic solution. There is always uncertainty. However, the amount of uncertainty differs depending on the pattern. Ensemble forecasts systems can help quantify this uncertainty.

GFS T382 Orography

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Uncertainty in the initial conditions.

HPC routinely prepares a model diagnostic discussion twice a day using the 0000 and 1200 UTC model runs. The discussion can be found at #http://www.hpc.ncep.noaa.gov/discussions/pmdhmd.html. As part of the PMDHMD forecasters routinely compare the surface, 500-hPA and 250-hPa analyses of the pressures, geopotential height fields and winds to conventional upper air, aircraft observations and also overlay these fields over satellite imagery from the same time. Essentially, the forecasters compare the 00 hr forecast from the model to the observations. They then note any significant differences between the 00h forecast (f00) and the observations.

Missed surface low

Erroneous low, pressures are 10-hPa lower than observed.

The figure shown above is an example where the model f00 differed significantly from the observations. The model did not catch the surface low that had a central pressure of below 1010 hPa well to the southwest of where it erroneously depicted one. Also note that the satellite cloud pattern supports the missed low and not the erroneous low to the north. This is the type of difference that is worth noting.

Forecasters may note that the f00 from one of the two operational models looks “better” than the f00 (analysis) of the other model. One might therefore assume that the model that appears to have the seemingly more accurate f00 might end up with a better forecast of the synoptic features. In cases where there are obvious differences of the timing or strength of the system this might sometimes be true. However, this assumes that the meteorologist can then anticipate the non-linear evolution of the pattern given the initial differences, this may not always be true. Also, the seemingly more accurate f00 may not always lead to a more accurate forecast.

There are several problems inherent in trying to assess how well a model has initialized its forecasts. One is trying to determine how big a difference between the observed geopotential height or pressure difference and f00 is significant. A 2 or 3 hPa difference between the observed value and f00 near the center of a developing surface low may not always be significant as models need maintain vertical consistency between different types of data. Forcing the model to closely “draw for” observed wind or geopotential heights can actual lead to a degradation of the forecast by introducing spurious waves. Also, it is becoming increasingly difficult to judge the f00 as 99% of observations now come from satellites, mostly in the form of radiance data. There is no easy way to compare the radiance values from the imagery to the f00.

Missed observation

1200 UTC 24 Jan 2000 model initial analysis (f00) of 250-hPa wind speed (color fill, scale at right, in ms-1) and observed wind speeds from RAOBS (numbers)

The poster child for poor initial conditions leading to a major forecast bust is the Jan 25-26 east coast snow storm. The Peach Tree City observation was 62ms-1 compared to the f00 of around 30ms-1at 250-hPa. The large difference between the observation and the model first guess field caused the observation to be tossed out during the quality control process.

Infrared satellite imagery valid 06 UTC 25 Jan 2000

The operational models completely missed the development of the closed 500-Hpa low and the precipitation associated with it. Note that the weak shortwave off the coast on the forecast versus the closed low that was observed. The eta forecast its precipitation off the coast at 1200 UTC 25 January and instead the storm produced heavy snow from Raleigh, NC to Washington, DC

While the poor f00 of the wind field probably contributed to the poor model forecast. Modeling experiments (Zhang et 2002) conducted on the case in which individual sounding were withheld from the control analysis suggest that deficiencies in the initial conditions and subsequent poor forecasts of the snow storm were unlikely to have arisen from one or two crucial sounding being missed. The large mesoscale error growth probably was also a result of moist processes.
The results of the Zhang research is to suggest that it will often be difficult to choose a model based simply on differences between observations and the observed state as mesoscale error growth may be strongly dependent on model physics and the various parameterization scheme that are used by the models. The optimal approach to determining the possible impact of initial conditions on model forecasts is therefore an ensemble approach which perturbs both the initial conditions and various types of parameterization schemes.

Model Trends and predictability

Forecasters have often used the trends of models as a forecast tool. The supposition is that if the model is trending in one direction that the model is most likely to continue to trend in that direction. While this supposition may sometimes prove true, Hamill demonstrated that D(prog)/dt or model trends of 850-hPa forecasts showed no predictive skill. He also noted that there was only a small amount of information on the accuracy of forecasts from the differences between the lagged forecasts.

There are times, however, when model trends may be used. The three precipitation forecasts below are from three successive runs of the NAM. By themselves, the only conclusion a forecaster can make about the forecast situation is there is probably uncertainty in where the western edge of the precipitation shield and axis of heavy precipitation will end up. One of the exceptions for short term forecasts is when observations are suggesting that there are significant problems with the current model’s mass and wind field.

12-36 hr ETA QPF valid 1200 UTC 25 Jan 2000

00-24 hr ETA QPF valid 1200 UTC 25 Jan 2000

00-24 hr ETA QPF valid 1800 UTC 25 Jan 2000

-4

-3

500-hPA analysis valid 0000 UTC 25 Jan 2000

MSL surface analysis at 0000 UTC 25 Jan 2000, pressure fall greater than 4 hPa outlined in red , area of falls greater than 3 is outlined in orange.

Between 1800 UTC 24 Jan. and 0000 UTC 25 Jan., the largest pressure fall of almost 5 hPa occurred along the North Carolina. The pressure fall to the northeast of the developing low were half that. Also, The model forecast winds at 500-hPa near Wilmington, NC (ILM) were southwesterly at around 50 kts while the observed winds were south-southeasterly at 40 kts. The 90o difference in 500 wind direction has huge implications about the western edge of the precipitation field and suggests moisture would be pulled west of the forecast. The location of the pressure falls suggested that the low would track west of the eta track.
The other exception is when the majority of the ensemble guidance and other operational models are predicting the same trend. However, the agreement of the models does not mean that your deterministic forecast based on the consensus of model solution will be the correct one. It only means that the new solution is probably more likely than the old one.
Usually, big model flip flops are not due to marked changes in the initial conditions from what is expected but instead is related to the predictability of the pattern.

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36 hr forecast of the 500-hPa geopotential heights valid at 1200 UTC 25 Jan 2000 (purple) and the observed for the same time (orange)

Ensembles

Edward Lorenz (1963) noted that small difference in the initial state could lead to huge differences in the resultant forecast. Atmospheric processes are non-linear so small differences in the initial conditions can lead to significant differences in the end solution. Unfortunately, there will always be significant uncertainty in the initial conditions. Data assimilation requires large amounts of data to be ingested into a numerical model with various types of data having different error characteristics. Quality control needs to be applied to this data in such a way as too minimize errors. However, the quality control if too stringent may sometimes smooth data points or if it is not applied sufficiently may introduce spurious waves. Ensemble forecasting attempts to account for some of the uncertainty in the initial conditions allowing a forecaster to look at the impact that slight differences in initial conditions may have on a forecast.
A number of atmospheric processes also occur at a scale below the scale that the operational models can resolve so these processes are parameterized. When you parameterize a physical process you essentially are approximating the effects of the physical process on the atmosphere which introduces small errors that can grow with time. Therefore, ensemble forecasts systems need to also vary the physics or parameterization schemes between the various members.
Finally, even the mathematics used in the model may introduce small differences. For example, the mathematics in a spectral model are different than those in a one that used finite differencing. UCAR offers a matrix of the ways that the MREF (now GEFS) and SREF are perturbed (see http://meted.ucar.edu/nwp/pcu2/ens_matrix/). Essentially, the GEFS ensembles initial conditions are perturbed but not the physics or mathematics as all the models use the same parameterization schemes for convection, radiation, etc., and all members are also spectral models. Essentially, the GEFS members have the same physics and mathematics as the GFS. By contrast, the SREF members have both the initial conditions, physics and mathematics perturbed. Therefore, the SREF members should have more diversity among its members than the GEFS. However, even SREF at times will not have sufficient diversity or spread.
Two UCAR sites offer training of ensemble forecast systems. The first training site is an introduction to ensemble forecasting that explains the strengths and weaknesses of using spaghetti diagrams and ensemble mean products (http://meted.ucar.edu/nwp/pcu1/ensemble_webcast/). One important point the training package makes is that when using ensemble guidance to assess the probability of an event, be sure to take into account any biases that the model may have. For example, the tendency for the GFS and RSM members have to be too far north with their precipitation maxima during convection with strong systems. The second site offers a little more advanced training packet on the use of ensembles (http://meted.ucar.edu/nwp/pcu1/ensemble/print.htm). These UCAR modules should be completed as the use of ensemble forecasting techniques will increase as the NWS transitions to probabilistic forecasts.

Another important thing to remember is that the various ensemble members have a significantly lower resolution of the terrain than the operational models especially the NAM. Therefore, near the mountains the qpf from the individual members will also lack resolution and will often underpredict precipitation along the windward slopes of the mountains and may also overpredict precipitation to the lee of the mountains. The GEFS members have the most degraded terrain representation of the ensemble members. Note in the graphic below how it fails to depict the coastal mountains in California and also does not depict the Sacramento Valley. The various WRF members and RSM members of the SREF have slightly different representations of terrain but all have a resolution of either 40– or 45-km. The figure at top right shows their approximate resolution. The eta members of the SREF have a 32 km resolution (below left). Note that all the Sref members have much lower resolution than the 12-km NAM (below right). Note how much smoother the terrain of all the ensemble members compared to the 12-km NAM. If you expect a precipitation maximum associated with a terrain or land-sea interactions like the sea-breeze, use the higher resolution models. The PQPF for higher thresholds associated with these smaller scale features, the ensemble members will underforecast the probabilities.

GEFS members terrain

Approximate resolution of terrain in RSM and WRF SREF members

Eta SREF members terrain

12 km NAM terrain

USWRP has recognized the need for methods to assess the predictability of warm-season convective events (Fritsch and Carbone, 2004) and cool-season storms (Ralph et al., 2005). Both articles stressed the important role that ensemble methods would have in helping determine the probability of event occurring. Each also note the need for probabilistic forecasts. Undoubtedly, ensemble methods will be provide one of the backbones for producing these forecasts. Ensemble methods are already being used by the HPC Winter-Weather Desk to help assess the probability of heavy snow and icing.

In later sections, exercises will be presented on using ensemble guidance in the forecast process to help identify the potential for extreme rainfall events. However, the next two sections will provide an overview of the performance of the GFS and NAM at predicting QPF.

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GFS model

The GFS model has had a resolution of about 40 km since May 2005. When using the model it is important to remember that its resolution is still not capable of resolving some mesoscale features. The resolution of the model limits the scale of the diabatic physical processes that can be are resolved and maintained by the model. For addition discussion of resolution issues see
http://www.meted.ucar.edu/nwp/pcu2/avhres3.htm
In the fall of 2006 the GFS still used a rather simple cloud and grid scale precipitation parameterization scheme (Zhao and Carr 1997). This scheme predicts cloud liquid and ice based on relative humidity and temperature at each layer. It allows clouds to be advected but does not allow hydrometeors to advect as they fall. Therefore in cases where strong warm advection is taking place, the model may be too slow to spread precipitation north or northeast with the flow. However, the GFS has a high bias for lighter rainfall amounts that may somewhat offset the effects of the lack of horizontal advection of the hydrometeor. A simple scheme usually will do a relatively good job of predicting the precipitation when large scale synoptic forcing is present providing that the model has a decent mass (pressure, temperature and height) and wind field forecasts. A more complete discussion of the simple scheme that is/was used in the fall of 2007 is available at http://www.meted.ucar.edu/nwp/pcu2/avncpmp1.htm
The GFS is expected to move to the Ferrier scheme in 2007, the same scheme used in the nam/wrf. However, currently it still uses a simple cloud scheme.
All the precipitation falls in one time step so the effects of dry advection on hydrometeors is ignored. Dry layers near the surface may reach saturation too soon. Also, behind a surface low where cold advection is occurring, the GFS often forecasts a large area of light precipitation and overpredicts light precipitation as there is no efficient mechanism to allow the hydrometeors to evaporate before reaching the ground.

The Arakawa-Schubert (A/S) convective parameterization scheme is used in the GFS (and in the RSM members of the Short Range Ensemble Forecast System (SREF)). Details about the scheme can be found at the UCAR model matrix site (http://www.meted.ucar.edu/nwp/pcu2/avncp1.htm). There are a few things a forecaster should realize about the A/S scheme. For example, the CAP is not directly considered. However, when the scheme is activated, updrafts are entrained to the LFC which acts to reduce CAPE when dry air is present in association with a CAP. The scheme can predict elevated convection but only approximately the lowest 300-hPa of the atmosphere. The scheme is very susceptible to grid scale convective blow-ups when the airmass is very moist and unstable. The feedback associated with these blow-ups tends to produce the overdevelopment of frontal waves. The blow-ups also tend to occur later than the actual convection and over the sloping baroclinic zone. This usually leads to the grid scale bomb being north of the actually precipitation maximum. The observed maximum is usually located closer to the warm air and region of higher precipitable water than the predicted maximum which is often located over sloped isentropes associated with the frontal zone.
The GFS overpredicts total rainfall in cold and warm seasons.

While the two graphs above only show the bias for one winter and summer month, they are very representative of the differences in bias characteristics of the GFS between the cold and warm seasons. The GFS bias for 0.01 inches or more over the U.S. during the period from November through March varied between 1.8 and 2.2 for all 6 hour periods. The bias during the warm season for 0.01 inches or more was generally around 1.6.

The high bias for lighter amounts shows up clearly during the cold season over the northeast. This can lead to problems when trying to predict snowfall amounts as light precipitation during several 6-hourly periods can lead to 24-h totals of over 0.25 or greater amounts. However, some of the problems may also be due

to problems gauges have in catching snowflakes which often causes an liquid equivalent amounts to be questionable and probably lower than they ought to be.

The 24 hr bias for heavier amounts tends to be high during the months with the greatest moisture and instability. The high bias for the 2.00 inch greater amounts probably is related to the grid scale feedback problems discussed earlier. The bias over the CONUS for the precipitation thresholds greater than 0.50, 1.00 and 2.00 is greater for the day 1 period (12-36 hr forecast) than for the day 2 or day 3 forecast with the bias for these thresholds decreasing as the lead time of the forecast increases.

Day 1 bias for 1.00” or greater amounts

Day 3 bias for 1.00” or greater amounts

There are also considerable regional differences in the GFS performance. During the cold season when you combine 6-hourly QPF during the day 1 forecast, the GFS has a high bias for even the high precipitation threshold

In winter, the GFS tends to overpredict the areal extent of 06 hourly QPF for most thresholds across MBRFC area and to a lesser extent the upper Mississippi Valley regions. This often results the in the model overpredicting the 24 hour precipitation across these regions.

Farther south (see above), the GFS underpredicts the 0.50 inch or greater amounts during the combined 6 hourly periods of the day 1 forecast and significantly underpredicted the 1.00 inch or greater amounts during the Jan-Feb period especially at the longer time ranges.

12-36 hr scores over Gulf Coast Region
3 month verification Sept-Nov

60-84 hr scores over Gulf Coast for Region for 3 month period Sept-Nov

A 3-month verification over the Gulf coast region suggests that GFS and NAM/WRF bias for heavier precipitation decreases with time with the bias being quite low in day 2 and 3 time ranges. A similar bias was noted during the spring period.

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GFS model performance at predicting QPF (continued)

12-36 hr scores over Midwest Region for Region for 3 month period Sept-Nov

The bias is much higher over the Midwest region than the gulf coast region form almost all thresholds and time ranges. The GFS appears to have high bias across the northern states and a low bias for heavier amounts across the south, especially at the longer time ranges. A subjective verification of the GFS during the March-June 2006 period by Faye Barthold found that during 45 percent of the GFS forecasts of rainfall maxima east of the Rockies that the GFS predicted the maximum too far north and made a had fairly accurate prediction of the maximum 34 percent of the time. Only 6 percent of the time was the maximum too far south. When strong shortwaves are present, the GFS tends to predict too much precipitation with the upper low or upper trough and too little precipitation ahead of the trailing cold front across the southern states. The same bias was present during the fall of 2006.

24 hr observed rainfall (in mm) ending at 1200 UTC 21 March 2006

36-60 hr GFS accumulated precipitation valid 1200 UTC 21 March 2006 (in mm)

The observed and forecast maps above illustrate the typical bias of the GFS. Note that across the Kansas near where the upper low would be located that the model predicted a an area of 25 mm (1.00”) or greater precipitation compared to the much smaller area that was observed. Some of the difference may be due to problems measuring snow which is not captured well by ASOS, however most of the observed bias probably is related to the model overprediction as it suffers from the same bias during much of the year. Farther south the model did a decent job of forecast the size of the areal coverage of the 25 mm or greater amounts. However, the model predicted its maximum well downstream (east-northeast) of where the axis of heaviest rainfall was observed.
The UCAR Model Matrix discussion notes that grid-scale precipitation bulls-eyes result from insufficient model convection. The discussion notes that these bulls-eyes typically develop later than when convection should actually develop. Therefore, these maxima often occur over the sloping frontal surface or downstream from where the convection actually occurs which is near the front in or near the warm sector.

The GEFS ensemble members currently use the same simple cloud scheme as the GFS and also a simplified version of the Arakawa-Schubert Convective parameterization scheme. This suggests that un-bias corrected or calibrated members are liable to have the same performance characteristics as the GFS. If true, using it to help assess where to predict the axis of heavy rainfall may be problematical. It’s also important to realize that the A/S scheme will not accurately simulate outflow boundaries. This is another source of possible error.

A grid-scale feedback bomb

Under-development

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60-84 hr scores over Midwest Region for Region for 3 month period Sept-Nov

Terrain and resolution issues

The two graphics above are the new GFS terrain. Note that it now clearly defines such terrain features as the Sacramento Valley and Columbia river valley of the Cascade range in Oregon and Washington and the Hudson Valley.
The GFS uses a mean terrain. The Mountain tops in the models are lower than the true terrain and valleys are often higher. highest terrain in the Rockies is nearly 5000m but the height in the smoothed model terrain is only 3500m. The Appalachians are depicted as being between 800-1050m in the smoothed mean average model terrain but the highest terrain is actually between 1000-2000m. The figure below is an idealized example of how the differences between actual terrain and model terrain might impact on a quantitative precipitation forecast.

Precipitation in complex terrain is often strongly tied to terrain and orographic lifting. Precipitation is enhanced on the upwind side of the mountains while there is often a rain shadow on the downwind side. In illustration above, grid point 1 would not be impacted by down-sloping winds but a town in the valley (point A) would probably would and would probably receive less rain or snow than forecast by the model. For the ski resort at point B, the snowfall would be heavier than predicted at the nearest model point (2). You can also imagine how the mean terrain can deleterious effect on a low level temperature forecast for valley and mountain locations.
The GFS forecast (below right) is about as good as you can expect from a model with the mean model terrain and a resolution of about 40 km.

Legend in mm

Observed 24 hour precipitation ending 1200 UTC 7 Nov. 2006

12-36 hr GFS QPF valid 1200 UTC 7 Nov. 2006

PRISM yearly rainfall climatology (left) and legend

The model did a good job of predicting that the event would be a heavy one and did a good job of forecasting the generally area where the heaviest rainfall would be located. However, the model did not forecast heavy enough rainfall maxima over the coastal mountains or over the Cascade range. Note that the location of the observed precipitation maxima along the Oregon and Washington coast were very similar to the maxima on the PRISM climatology. The model did not adequately depict the rain shadow to the east of the Cascades or depict the break in heavier rainfall between the Blues of southeastern Washington/northeastern Oregon and the higher terrain of Idaho.

The GFS also lacks to explicitly predict the convective plumes associated with lakes effect events. Note the radar echoes to the lee of the southern part of Lake Michigan compared to where the GFS is predicting However, the increase from a T-170 (approximately the resolution of a model with 80-km grid spacing) to T-382 (roughly the resolution as a model with a 40-km grid) now allows it to at least forecast enhanced low-level lifting to the lee of the lakes.

Observed radar composite valid 0000 UTC 9 Dec 2006

12-hr GFS forecast valid 0000 UTC 9 Dec 2006 of delta T between 2-m and 850-hPa, wind in the lowest 30-hPa, RH in the 60-90 hPa above ground layer and omega in the 150-180-hPa above ground layer.

The various SREF members have a similar horizontal resolution and the GFS so they should have many of the same issues.

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The NAM model

The NAM model no longer uses the eta coordinate system and instead used a hybrid sigma coordinate system. There are a number of advantages to the new coordinate system, see
http://www.meted.ucar.edu/nwp/pcu2/namvcoor1.htm
The one disadvantage of the use of a terrain following scheme near the surface typically is it results in the scouring of cold air to the lee of the mountains and insufficient blocking of the flow directed towards the mountains. Such blocking is an important mechanism in cold air damming. Another recent change to the NAM model has been the introduction of the Ferrier cloud and precipitation parameterization scheme. This method is a more complex parameterization scheme than the NAM used previously but is still not considered a complex scheme. However, it now has four different classes of hydrometeors, mixed clouds, large ice (snow, grapple and sleet), small ice (suspended cloud ice). It also allows for the vertical advection of the total condensate but does not advect different classes of condensates separately. The scheme also allows for both warm and cold rain processes.
Despite the improvements compared to its previous cloud and precipitation parameterization method. The new scheme does not adequately handle some phenomena. For example, heavier precipitation rates associated with rapid dendritic crystal growth are not simulated. Also, it does not allow crystal growth where an inversion is present and instead actually has the crystal shrink slightly. The differences in the droplet spectrum between continental and marine airmass are not adequately handled which can play a significant role in modulating collision-coalescence processes. Even with knowledge of these shortcomings, it is difficult for a forecaster to judge their impact on individual precipitation forecasts.
The convective parameterization scheme currently employed in the WRF/NAM is the Betts-Miller-Janjic (BMJ) scheme. When the parameterization scheme kicks in it uses reference profiles with thermal profile similar to that of the moist adiabat and with dewpoint depressions of 4 to 6oC. The following website explains the reference profiles,
http://www.meted.ucar.edu/nwp/pcu2/etacp3.htm.
It’s important to remember that convective parameterization schemes were not developed to produce true convective rainfall rates but were developed to keep models from producing convection on the scale of a grid box. Such grid scale convection would to significant erroneous feedback into the mass and wind fields of the models. The precipitation produced by a parameterization scheme is a by-product of the scheme not a true forecast of the rainfall associated with convection.
The scheme only triggers when there is an unstable layer in the lowest 200 hPa above the ground and the convective cloud depth is at least 200 hPa. The adjusted profiles must produce warming and drying and the pre-convective precipitable water in the cloud layer must be higher than the precipitable water in the adjusted reference profile. Therefore, the NAM often has problems forecasting elevated convection.
The classic severe weather sounding with a dry mid layer may not trigger the scheme as the precipitable water in the cloud layer may not exceed the precipitable water of the adjusted reference profile. This can lead to an underprediction of convection and its associated rainfall when a classic loaded-gun sounding is present.
One drawback of the scheme is that it convects even if a cap is present. The scheme can then activate with low-level CAPE but activates before the convective temperature is reached. This leads to the model forecasting convection too early in the day. This may explain why the WRF/NAM tends to have a high bias for lighter thresholds of rainfall but a very low bias for higher ones across the south during summer. This low bias for heavier amounts seems to occur more often where synoptic scale forcing is weak. By activating too early, the scheme prevents high CAPE from building up in the model sounding later in the afternoon. Note the low bias for June across the Lower Mississippi valley RFC area of responsibility. This bias is common when there is plenty of moisture and instability present but when the synoptic and meso-alpha scale forcing is weak.

Model performance and biases differ by geographic region. The National Precipitation Verification Unit (NPVU) maintains a site in which you can look at the verification of HPC, RFC, GFS and NAM precipitation forecasts broken down by NWS RFC region. The following website offers the verification statistics.
http://www.hpc.ncep.noaa.gov/npvu/qpfv/
A visual comparison of individual days is possible using the following website.

http://www.hpc.ncep.noaa.gov/npvu/qpfv/

http://www.hpc.ncep.noaa.gov/rgnscr/verify.html

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Ensemble guidance (continued)

Using climatological anomalies as a forecast tool

Hart and Grumm (2001) have used standardized anomalies to rank synoptic scale events from a climatology developed from the NCEP reanalysis. The standardized anomalies essentially scale the anomaly by the its standard deviation from the mean. The beauty of using standardized anomalies (SDs) is that the standard deviation provides information about the frequency of occurrence of whatever forecast parameter than you are looking at. As noted by Grumm and Hart (2001), a departure from normal of 2.5 SDs implies that the observed value occurs between 5% and 16% of the time. For fields like 500-hPa or 700-hPa heights the actually confidence limit is probably close to that of a normal distribution. Neither precipitable water (PW) nor the magnitude of 850-hPa magnitude of the moisture flux (MF)have a normal distribution, but even they appear to have confidence limits near that of a normal distribution. Note how over 99 percent of the time, the PW and 850-hPa magnitude of moisture flux is below 3 SDs.

From Junker et al. 2007

Magnitude of moisture flux

Precipitable water

Histogram showing the percentage of the distribution of PW and 850-hPa magnitude of moisture flux.

The 700-hPa height and standardized anomaly pattern for three different multi-day extreme rainfall events are shown below. Note that each has a greater than 3 SD height anomaly off California associated with it. The greater than 3 SD departure from normal suggests that 99% of the time during this time of year, the 700-hPa geopotential height anomaly is less than the observed anomalies during these three events.

-3 SD

-3 SD

-3 SD

700-hPa geopotential height and standardized anomaly of the height field (shaded) valid 0000 UTC 9 Jan 1995.

700-hPa geopotential height and standardized anomaly of the height field (shaded) valid 1200 UTC 9 Mar 1995.

700-hPa geopotential height and standardized anomaly of the height field (shaded) valid 1200 UTC 31 Dec 1997.

Heavy west coast precipitation events during winter usually are associated with a fairly strong negative anomaly with a weaker positive height anomaly to the southeast. All three of these multiday extreme rainfall events were associated with such a pattern which pretty much assures a strong southwesterly geopotential height gradient. Lackmann and Gyakum (1999) found a similar pattern of 500-hPa height anomalies with a similar couplet associated with heavy cold season precipitation events in the northwestern U.S. They noted that during their cases, this anomaly pattern often resulted in warm, moist air from near the Hawaiian Islands being transported northeastward into Washington and Oregon. Bell and Pereira (2005) found a similar 500-hPa and 700-hPa anomaly pattern associated with heavy rainfall events over the mountains of southern California. These various research efforts establish that the 500-hPa, 700-hPa geopotential height and anomaly patterns are very similar for all heavy precipitation events along the west coast with the primary differences are the north-south displacement of the anomalies

There were other indications that these three northern California events would be major rainfall events. The deep southwesterly flow associated with the pattern tapped into moisture in the subtropics which led to the anomalously high PW values being pulled northeastward. Two of the three events was associated with greater than 3 SD anomalies and the third with a 2.5 SD (not shown).

3 SD

4 SD

1200 UTC 9 Mar 1995 PW (contour) and Normalized PW anomaly (shaded, unit=1 SD.

1200 UTC 31 Dec 1997 PW (contour) and Normalized PW anomaly (shaded, unit=1 SD.

All three events were associated with plumes of moisture that impinged upon the coast. The role of these plumes or atmospheric rivers is even more evident when anomalies of the magnitude of 850-hPa moisture flux is examined. Let’s first look at the moisture flux anomalies associated with the January and March 1995 events (see below). The shaded areas are anomalously high MF anomalies with the white area having a 5 SD departure from normal.

5 SD

5 SD

The 5 SD moisture flux anomaly impinging upon the coast suggests that the both 1995 rainfall events might be relatively rare as more than 99.999% of the time the moisture flux will be lower than on the two maps above. Significant flooding and flash flooding also occurred over the Sacramento, Napa, and Sonoma Valleys as a result of rainfall during the eight-day period, January 7-15, 1995 (NOAA 1995), prompting thirty-four counties to be declared federal disaster areas. The March 9-10, 1995 caused considerable flooding on the San Joaquin basin (Reynolds 1996). The combined damage from the January and March 1995 floods exceeded 305 million dollars in the Sacramento Valley and 193 million in the San Joaquin Valley (Army Corps of Engineers 1999).
Let’s explore the 850-hPa moisture flux and MF anomalies during the period from 0000 31 Dec. 1996 through 0000 UTC 2 Jan. 1997 and explore the relationship of the maximum rainfall to the axis of strongest 850-hPa MF. During the entire period, the standardized MF anomalies were at least 4 SD and usually 5 SD. Again the normalized anomaly pattern signalized that there was the potential for a relatively rare, extreme rare precipitation event over California. This particular event, caused nearly two billion dollars in property damage and forced over 100,000 people from their homes (Baird and Robles 1997).

850-hPa moisture flux (arrow, scale shown at bottom of image), magnitude of moisture flux (interval=0.05 ms-1) and normalized anomaly of moisture flux (magnitude of the anomaly is shaded with the scale on the right hand side of the figure, interval=1 SD) for a) 0000 UTC 31 Dec. 1996, b) 1200 UTC 31 Dec. 1996, c) 0000 UTC 1 Jan. 1997, and d) 0000 UTC 2 Jan. 1997.

During upslope events, the anomalously moisture flux impinging upon the coast and mountains helps determine the location of where the heaviest rainfall will occur. The axis of strongest moisture flux was located north of San Francisco during the 24 hour periods ending at 1200 UTC 31 Dec. 1996 and 1200 UTC 01 Jan. 1997. By 0000 UTC 2 Jan. 1997 the axis had shifted south of San Francisco, only then did very heaviest rainfall shift south of the city (see figures below).

24 hr HPC precipitation analysis for, a) 1200 UTC 31 Dec 1996, b) 1200 UTC 1 Jan 1997, and c) 1200 UTC 2 Jan 1997.

All three of these multi-day rainfall events was associated with a “pineapple express” or atmospheric river where there was anomalously high PW and MF. Ralph et al. (2006) seven floods on the Russian River that were associated with landfalling atmospheric rivers attesting to their importance in producing excessive rainfall.

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Standardized 850-hPa moisture flux (arrow) and magnitude of moisture flux field at the beginning of the period of heavy rainfall, a) for 4 inch or greater events and b) for 2-4 inch events; at the end of the period, c) for 4 inch or greater events, and d) for 2-4 inch events.

There are differences in the moisture flux and PW anomaly fields between events that produce heavy rainfall compared to those that produce more modest amounts (see above). The differences in the anomaly fields for moderate and heavy support the idea that anomalies can be used to identify major rainfall events. The similarity of the 500-hPa, 700-hPa and 850-hPa moisture flux patterns at locations from the Pacific Northwest (Lackmann and Gyakum) and southern California (Bell and Pereira 2005) to the anomaly patterns shown above for northern California suggest that standardized anomalies may also be a useful forecast tool along the entire west coast.

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850-hPa moisture flux (arrows) and magnitude of the moisture flux (solid) and anomaly of the magnitude of the moisture flux (shaded, unit= 1SD) valid 0000 UTC 9 Jan 1995.

850-hPa moisture flux (arrows) and magnitude of the moisture flux (solid) and anomaly of the magnitude of the moisture flux (shaded, unit= 1SD) valid 1200 UTC 9 Mar 1995.

An example of how you might use standardized anomalies to make a forecast.

Pattern recognition remains an important component of predicting extreme rainfall events in the mountains along the Pacific coast of North America. The 500-hPa geopotental height and associated anomaly pattern is very similar for all heavy rainfall events with the primary difference being associated with the location of negative anomaly associated with the trough and the positive anomaly to the southeast. Note how similar the anomaly pattern associated with the heavy precipitation events in the Pacific Northwest (below right) to the pattern associated with heavy events in northern California (below right) at the beginning of the period of heavy precipitation. These similarities suggest that once a forecaster is able to recognize the ingredients associated with heavy rainfall in one place along the west coast, he or she should be able to translate that knowledge to enable them to identify possible extreme events in other places along the west. coast

From Lackmann and Gyakum 1999

Pacific Northwest composite

Northern California composite

Composite mean 500-hPa departure from normal (positive departures are solid, negative departures are dashed, contour interval=3 dam). Shaded areas are the 95% and 99% probably that the composite belongs to a distinct population from climatology.

Composite mean 500-hPa normalized departures from normal. Unit is 0.3 standard deviations. Negative values are below normal heights, positive are above normal heights.

Now we’ll attempt to apply pattern typing and the use of normalized anomalies to forecast a rainfall event over the Pacific Northwest. The first step is to compare the forecast geopotential height field to the composite for heavy events over that area. Note the similarities between the GFS forecast 500-hPa geopotential height field to the composite (below). Both exhibit a fairly strong trough in the height field over the Gulf of Alaska and distinct ridging over California.

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12-hr GFS forecast of 500-hPa geopotential height and vorticity valid 1200 UTC 6 Nov. 2006

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12-hr GFS forecast mslp and 100-500-hPa thickness (dashed) valid 1200 UTC 6 Nov. 2006

Also note the basic look of the forecast mslp pattern with both the surface low position in the Gulf of Alaska and the ridge axis extended from California west-southwest well into the Pacific and compare it to the composite (dashed line, above left). The axis of the area of strongest surface pressure gradient is also similar. However, there is a notable difference, the gradient is much stronger in the forecast than in the Lackmann et al. composite.

In their article, they defined a heavy rainfall day as one in which Seattle (SEA), Olympia (OLM) and Stampede Pass (SMP), WA and Astoria (AST), OR all received 0.50 inches of rain. Heavy but certainly not extreme precipitation. The surface pressure gradient suggests that the precipitation might end up being considerably heavier than that. The long fetch implied by the pressure gradient also argues that a strong low level jet may help pull a plume of tropical moisture east-northeastward.

24-hr GFS forecast of PW and 850-hPA winds valid 0000 UTC 7 Nov. 2006

The GFS forecasts of PW and 850-hPa winds indicate there is an impressive fetch of deep moisture and that there is definitely and atmospheric river associated with the event. One way to check if the model appears correct is by accessing the CIRA blended TPW product at
http://amsu.cira.colostate.edu/TPW/global.htm. During this case, the blended product supported the model forecast PWs. The “pineapple express” or atmospheric river was going to be a player in the forecast.
The strong 850-hPa winds coupled with the high PW suggest that higher than normal 850-hPa and 850-700-hPa moisture flux is present.

From Pandey et al. 1999

The figure at left illustrates the importance of low level moisture flux in modulating the precipitation in the Sierra Nevada mountains of California. It also suggests that the largest differences in moisture flux for different type of events is at or just below 800-hPa.

Mean distribution of vertical profile of moisture flux for Nov.-Jan. at Oakland during storm events in the Sierra.

An experienced forecaster would probably recognize that there was above normal PWs within this river but he or she still might not have an objective way of knowing how often such a plume occurs or how uncommon such high PWs are. The individual would have an even tougher time assessing a less commonly used diagnostic tool like moisture flux. Standardized anomalies offer a way of assessing how often the values of a particular meteorological field that are being forecast by the numerical models is present providing that the model has not observable bias in predicting that particular field.

12-36 hr QPF from the NAM model, scale in inches at left.

12-36 hr QPF from the GFS model.

Both the NAM and GFS predicted a significant rainfall event, however, the areal distribution of the rainfall differed between the models. The model with the better horizontal resolution of the terrain predicted less rain then the coarser resolution GFS. Both models predicted a relatively uncommon event but neither predicted amounts that suggested record rainfall might occur.

SREF ensemble mean PW (brown lines, in inches) and standardized PW anomaly (dashed red) valid 1200 UTC 6 Nov 2006

SREF ensemble mean PW (brown lines, in inches) and standardized PW anomaly (dashed red) valid 0000 UTC 7 Nov 2006

SREF ensemble mean PW (brown lines, in inches) and standardized PW anomaly (dashed red) valid 1200 UTC 7 Nov 2006

The SREF ensemble mean forecast of of a standardized PW anomaly of greater than 3 standard deviations (SD) is impressive since ensemble means average values so small differences in the location of the PW axis between the members tend to produce a broader moisture band and with lower maximum precipitable water than the individual members. Therefore, the high standardized anomalies suggest that there was strong agreement between the SREF members as to the location and movement of the band. The 3 SD anomaly or greater PW anomalies also indicate that over 99 percent of time the observed precipitable water is lower than is being forecast suggesting that the PWs being forecast are in the top 1% of events occurring during the same time of year.

SREF 850-hPa moisture flux (g/kg) ms-1 and standardized magnitude of moisture flux anomaly valid 1200 UTC 6 Nov. 2006.

SREF 850-hPa moisture flux (g/kg) ms-1 and standardized magnitude of moisture flux anomaly valid 0000 UTC 7 Nov. 2006.

SREF 850-hPa moisture flux (g/kg) ms-1 and standardized magnitude of moisture flux anomaly valid 1200 UTC 7 Nov. 2006.

The SREF ensemble mean 850-hPa moisture flux anomalies are even more impressive reaching over 5 standard deviations. Such a high standardized anomaly suggests that November observed moisture flux has rarely, if ever, exceeded the forecast values since 1950. Unless the SREF ensemble mean forecast of moisture flux has a high bias, this has the potential to possibly be a rare event. Such a slow moving moisture flux axis with such anomalously high moisture argues that this event might have the potential to be a historic one.

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Composite mean 500-hPa height (solid) and sea level pressure (dahsed , every 4 hPa) at the beginning of the period of heavy precipitation over the Pacific Northwest.

12-hr GFS forecast of PW and 850-hPA winds valid 1200 UTC 6 Nov. 2006

Knowing that the SREF predicted moisture flux anomalies are suggesting that a rare heavy event might be in the offing, how might the forecaster modify the model forecasts below?

12-36 hr QPF from the NAM model, scale in inches at left.

12-36 hr QPF from the GFS model.

One possible method would be to employ the Hamill and Whitaker (2006) reforecast analog products that employ a low resolution version of the GFS for its individual members and then uses the resultant ensemble mean QPF and PW fields to identify analogs that can be used to construct an ensemble suite. Such a method then can be calibrated to produce bias adjusted probabilities for various precipitation thresholds. The method also can be used to provide a guidance product that tells the forecaster the precipitation amount that was only exceeded in 10 percent of the cases identified by the reforecast analog technique for that particular day. The graphic below left is such a product for the 24 hrs ending at 1200 UTC 7 Nov. 2006.

24-h analog ensemble QPF-90th percentile (upper decile) (in) product valid 1200 UTC 7 Nov. 2006.

The greater than 3 SD and 5 SD departure from normal for PW and 850-hPa moisture flux predicted by the SREF ensemble mean suggests that this event will at least equal the 24-h QPF from upper 10% of the reforecast project analogs (upper left). Therefore, in the area where the strongest moisture flux is aimed the longest, the precipitation amounts should be similar if not a little higher than suggested the reforecast analog product.

Compare the analog forecast amounts to those predicted by GFS and NAM in the area enclosed by the circle and compare those amounts the high resolution precipitation analysis for the same time period (below).
Essentially, the analog method had the best forecast in the area enclosed by the circle. Note that the analog predicted over 8 inches in the Olympic mountains where over 10 was reported but where the GFS only predicted 3. Also, it did better than either the GFS or NAM in the mountains along the coast of northwest Oregon. During this period, Lee’s Camp in Oregon received 14.3 inches and June Lake in Washington received over 15. Record flooding was reported on 10 rivers and 16 counties were declared to be in a state of emergency by the governor or Washington. The standardized anomalies correctly identified a rare event.

24-h accumulated precipitation (4 km grid) valid 1200 UTC 7 Nov. 2006.

24-h accumulated precipitation (32 km grid) valid 1200 UTC 7 Nov. 2006.

Where analog method fails is that it spreads the heavy rainfall too far south in Oregon. Also note that in areas outside the circle the analog technique tended to forecast too much rainfall. The method tends to predict too large an area of heavy rainfall because of the way it identifies heavy cases. It also tends to forecast too much precipitation to the lee of the Cascade range where the rain shadow is typically located. For the area extent of the more moderate amounts, a position near the middle of the two higher resolution models usually will be better than the analog. The way the analog method downscales the precipitation using mountain mapper also tends to cause it to overpredict precipitation in the mountains in regions away from the axis of stronger moisture flux and orographic lifting. The NAM and GFS will usually provide a better idea of the general coverage of the precipitation than the analog product but the latter will often offer a better guess at the maximum rainfall when the moisture flux and PW are highly anomalous.

Another tool providing by the analog ensembles is a calibrated probability for various precipitation thresholds (a PQPF). This tool is available through day 10 on the HPC workstations in NMAP. The example at right provides the calibrated PQF for 4.00 inches during the 24-hr period ending at 1200 UTC 7 November 2006. The fact that the calibrated PQPF product was forecasting areas with greater than a 60% probability is telling since 4.00 events are a relatively rare occurrence.

For shorter range forecasts east of the mountains of the west, the analog products like the ensemble mean usually are not as reliable as the higher resolution model QPFs at the shorter time ranges.

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Another case...using ensemble guidance and standardized anomalies.

Standardized anomalies are a useful but not a stand-alone forecast tool. They should be used as and adjunct to an ingredients based approach to assessing the pattern and its precipitation potential and that many heavy rainfall events are associated with convection and mesoscale and storm scale processes. A number of factors that should be considered when trying to forecast extreme rainfall are discussed elsewhere
http://www.hpc.ncep.noaa.gov/research/mcs_web_test_test.htm.
First we’ll use the forecast funnel approach espoused by Snellman (1977) and look at the synoptic scale pattern and then work towards assessing the systems potential to produce a mesoscale heavy rainfall event.

24 hr NAM surface pressure and boundary layer wind and temperature v.t. 0000 UTC Oct. 27, 2006. The thick red lines is a warm frontal position.

A fairly cold airmass was located over the east and the NAM and GFS predicted the development of a low to the lee of the Rocky Mountains in western Oklahoma. The remains of an old frontal boundary was located over Louisiana. This boundary was predicted to move very little during the 12 hrs between the two forecast maps shown above. South of the warm front, surface dewpoints were forecast to be above 70oF to the south of the warm front across eastern Texas and over the western Gulf of Mexico.

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12hr GFS 500-hPa heights and vorticity valid 1200 UTC 26 Oct 2006.

27hr GFS 500-hPa heights and vorticity valid 0300 UTC 27 Oct 2006.

27hr GFS 250 heights and isotachs valid 0300 UTC 27 Oct 2006.

The first question to ask is this a synoptic or mesoscale pattern that you recognize as one favorable for heavy rainfall. In this case the synoptic pattern looks very similar to the Maddox et al. synoptic type heavy rainfall archtype. A front is approaching eastern Texas and Louisiana with very moist and relatively unstable air located across eastern Texas to its east.

Schematic diagram of surface pattern for Maddox et al. (1979) heavy rainfall events.

Schematic diagram of upper level pattern during Maddox et al. synoptic type events. Green lines are 500-hPa streamlines, winds are at 500-hPA, light blue is location of 250-hPa wind maxima.

The next step of the forecast process is to start an ingredients based diagnoses of the rainfall potential.

Sounding at 1200 UTC 16 Oct. 2006 for Lake Charles, Louisiana.

Remember that rainfall rates are determined by the vertical transport of moisture into the cloud system and and on the precipitation efficiency. The single most important factor in precipitation efficiency is the relative humidity. In this case, the airmass is unstable with sufficient CAPE to support fairly strong updrafts.

The positive area of the sounding is long and skinny rather than short and fat. There is very deep layer near saturation with around a 4 km above freezing layer which is favorable for warm rain processes and high precipitation efficiency. Note also that the forecast 850 winds at LCH below are considerably lighter than forecast by the GFS but that the precipitable water forecast is similar in magnitude.

12 hr GFS 850-hPa wind and PW forecast valid at 1200 UCT 16 Oct. 2006. Black dot is location of LCH.

24 hr NAM PMSL, PW and original CMF (Corfidi) vectors valid 0000 UTC 17 Oct. 2006. Barb=10 kts, half barb=5 kts.

Analysis of the observed and forecast soundings (not shown) suggested the potential for high rainfall rates. The CMF vector method suggested that any convective system would be slow moving, in the 5 to 10 kt range. Clearly, the pattern favors heavy rainfall somewhere across the south.

12-36 hr GFS QPF valid 1200 UTC 17 Oct. 2006.

That both the GFS and NAM forecast heavy rainfall across the region from eastern Texas across Louisiana into Texas is no surprise. In a sense, the two forecasts in a sense provide two members if an ensemble. Ensemble products provide an estimate of the uncertainty of the event and can provide a rough idea of the probability of an event occurring. In this case, the operational models indicated there is a high likelihood of a heavy rainfall event but differ on how heavy and where the maximum might be located. Ensemble forecasts from the SREF and GEFS are additional tools that can and should be used to assess the likelihood of this being a significant rainfall event.

24 hr 3 inch spaghetti diagrams valid 1200 UTC 17 Oct. 2006

SREF members, eta members are red/yellow, rsm members are blue/purple and wrf members are green.

GEFS members, only initial conditions are perturbed.

Based on the two sets of ensemble products and your knowledge of the GEFS and SREF ensemble guidance which is the least true.

A)

The ensembles members suggest that the NAM maximum rainfall may be too light.

B)

The ensemble members suggest that the highest threat for over 3-inches of rain extends along an axis from central and northern Louisiana into central or northern Mississippi.

C)

The SREF ensemble members will typically show more spread than the GEFS members.

D)

The ensemble members suggest there is a high probability of over 3 inches during the event and that there is some potential for over 5 inches.

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12 hr NAM surface pressure and boundary layer wind and temperature v.t. 1200 UTC Oct. 26, 2006. The thick red lines is a warm frontal position.

12-36 hr NAM QPF valid 1200 UTC 17 Oct. 2006.

Using ensemble guidance and standardized anomalies (continued).

Using the various ensemble mean forecast also can provide valuable clues to the forecast. An ensemble mean typically will not depict small areas with very high values as the ensemble averages all the members. Therefore, an ensemble mean qpf will rarely depict over 2 inches or rain in a 24 hour period. However, the mean can forecast the axis of the maximum rainfall providing the individual members are not biased. The beauty of ensemble means is that they tend to keep your axis between the extreme solution which minimizes your losses.

Ensemble mean forecast of standardized anomalies can also be very useful in assessing the probability of an extreme event. Since ensemble mean products tend to smooth fields and lower the maximum values for any fields forecast, the mean tends to only predict high standardized anomalies when the individual members are in strong agreement. Below are the SREF ensemble mean forecasts of PW and the standardized anomaly of the same field for the October 26-27 event.

15-hr SREF ensemble mean forecast of PW (brown, inches) and standardized anomaly of PW (dashed red) valid 1200 UTC 26 Oct. 2006.

27-hr SREF ensemble mean forecast of PW (brown line, in inches) and standardized anomaly of PW (dashed red) valid 0000 UTC 27 Oct. 2006.

15-hr SREF ensemble mean forecast of 850-hPa magnitude of moisture flux (scale at left, (g/kg)(ms-1), standardized anomaly of magnitude of moisture flux (dashed red) and 850-hPa wind (barbs).

27-hr SREF ensemble mean forecast of 850-hPa magnitude of moisture flux (scale at left, (g/kg)(ms-1), standardized anomaly of magnitude of moisture flux (dashed red) and 850-hPa wind (barbs).

The standardized anomalies, especially of the magnitude of the moisture flux suggested this could be a significant event. Over 99 percent of the time, the observed moisture flux values during the same time of year will be lower than the forecast values. The probability of the 5 inch or greater event forecast by the GFS is therefore probably quite a bit higher than suggested by either the GEFS or SREF member which forecast no 5 inch or greater members. While the standardized anomalies often aid in assessing the probability of a relatively rare heavy rainfall event, the anomalies do not help pinpoint the exact location which are usually focused by mesoscale or even storm scale processes.

24 hr observed accumulated precipitation valid 1200 UTC 17 Oct 2006. Magenta is over 6 inches, white over 10.

24 hr GEFS member 3-inch contours valid 1200 UTC 17 Oct. 2006.

24 hr SREF member 3-inch contours valid 1200 UTC 17 Oct. 2006.

The axis of the observed precipitation maximum was south of any GEFS member. This error highlights one of the shortcomings of using a group os ensemble members that lack sufficient perturbations. The GEFS members share the same convective scheme with the GFS and fellow GEFS members.

A 2006 study by Barthold indicated that the location of the GFS rainfall maximum was much more likely to be predicted too far north versus too far south. This is consistent with one of the known biases of the Arakawa Schubert scheme that is mentioned in the UCAR Model Matrix which note that bulls-eyes from the A/S scheme have their convection from too late and along the sloped frontal surface rather than near to the warm air where the actual convection usually develops. The SREF ensemble members offered a better idea of the threat area having it extend from southwestern Louisiana into southeastern Arkansas and northern Mississippi. However, its members will often cluster based on their convective schemes with the GFS and Kain-Fritsch members in one group and the Eta members that use the BMJ scheme in another so even the SREF members may not have enough diversity.

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References

Army Corps of Engineers, 1999: Post-flood assessment of 1983, 1986, 1995 and 1997 in the Central Valley, California. The Army Corps of Engineers’ Sacramento and San Joaquin Basins Comprehensive Study, Availalble for the U.S. Army Corps of Engineers, Sacramento District, 1325 J Street, Sacramento, CA 95814-2922.
Baird, B. P., and R. R. Robles, 1997: Emergency management issues in the California floods of 1997: lessons learned or lessons learned or lessons lost? Available form California Specialized Training Institute, P.O. Box 8123, San Luis Obispo, CA, 50 pp.
Bell, K. M., F. J. Pereira, N.W. Junker, R. H. Grumm and R. Hart, 2005:Forecasting heavy wintertime precipitation events in Southern California. AMS 21st Conference on Weather and Forecasting, Washington DC, P.192.
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