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Lessons Learned From Using The MPE Processing System. Gregory J. Story HAS Forecaster NWS West Gulf River Forecast Center. It II. Enhanced MPE – somewhat of misnomer Delivered in OB 8.3 Separate program from MPE Increased resolutions Temporal - Every 5 minutes (based on VCP)

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Lessons Learned From Using The MPE Processing System

Gregory J. Story

HAS Forecaster NWS West Gulf River Forecast Center

It II

  • Enhanced MPE – somewhat of misnomer
    • Delivered in OB 8.3
    • Separate program from MPE
    • Increased resolutions
      • Temporal - Every 5 minutes (based on VCP)
      • Spatial – 1/16 HRAP (1x1 km)
    • Uses radar data (DSP product)
      • Biases based on values from MPE
      • No ingest of gauge data
    • Fields not interchangeable with MPE
    • Designed to provide input into FFMPa

Problems With WSR-88D Precipitation Estimates (Why we bias the Radar)

It has been determined by the WGRFC HAS Forecasters that at least three primary things can have a great impact on the amount of rainfall that is estimated by the WSR-88D. One is radar calibration, another is proper Z-R relationships, and the third is the proper use or misuse of the WSR-88D adaptable parameters. Using MPE’s bias adjustment schemes, we attempt to mitigate the radar precipitation estimates as much as possible.

MPE Biases from WGRFC are now on the internet

Introduction

First Lessons Learned from Stage III

  • Radar precipitation estimates give spatial and temporal resolution not possible from using rain gauge data alone.
  • Radar precipitation estimates can be in considerable error.
  • Bias calculations to raise or lower radar precipitation estimates work well.
  • A means of obtaining precipitation estimates from beam blocked areas of New Mexico and southern Colorado, as well as Mexico and far southwest Texas, are necessary.
  • The WGRFC has noted periods of high radar biases in MPE which are not related to Z-R relationships or calibration issues. One factor which has come into play has been the overuse of ground clutter suppression. An example follows:
  • Note the low estimates of rain from radar throughout the KHGX Houston radar area.
  • The Gauge-Only Analysis suggests heavier rainfall near Houston.
  • The Gauge Table confirms radar estimates are way too low (bias of 3.15). Below is the bias corrected estimate:

The WFO’s in our area of responsibility can see what their radar’s bias

is in real time.

MPE Build 8 Beta Test

Over the past 10 years, the National Weather Service West Gulf River Forecast Center has been using real-time precipitation estimates produced by the WSR-88D radar. The radar precipitation estimates are very useful to the hydrometeorologists at the WGRFC for real-time rainfall estimates which in turn are used for flood prediction. Additionally, precipitation estimates using the network of GOES satellites (called the hydroestimator) has become available for use. Tools to enhance and correct the radar-derived precipitation estimates have been in use for some time now, beginning with the Stage III precipitation processing system in the 1990’s. The latest interactive program being used which optimizes the radar precipitation estimates by comparing them to hourly rain gauge reports, and by integrating the satellite precipitation estimates is called the Multisensor Precipitation Estimator (MPE) Processing System.

Recently this software has seen several additional fields added to it by the NWS Office of Hydrologic Development. The HAS forecasters at the West Gulf RFC have been using these fields during several rainfall events, including the newest fields from the AWIPS Build 8 version, which WGRFC is beta testing. I will describe what these fields are, how they are derived, and which fields appear to work the best under different types of rainfall events. I will also discuss how problems with WSR-88D precipitation algorithm settings can affect some of these fields, and ways the HAS forecasters mitigate these problems using MPE. The MPE program has also been deployed at the field offices around the country for use at the local level. To assist our field offices in their use of the software, our RFC has implemented a process to share their radars’ biases with them. This enables weather forecast office forecasters to see if their radar may be overestimating or underestimating the rainfall. This in turn assists them to issue more accurate flash flood warnings and aids them in using MPE with satisfactory results. I will illustrate how this field bias information is shared with the offices in the WGRFC area of responsibility. In addition, the Enhanced Multisensor Precipitation Estimator program is slated to be deployed at field offices in the near future. This program will use a much finer temporal and spatial resolution to provide precipitation input to the Flash Flood Monitoring and Prediction program used at NWS field offices. I will describe how MPE and EMPE are interconnected.

MP

MPE Advantages

The Multisensor Precipitation Estimator software gave WGRFC:

1. New fields, including:

Satellite Precipitation Estimates using the NESDIS Hydroestimator

Local Bias Radar Field Mosaic

Local Bias Multisensor Field Mosaic using MPE radar climatologies

Field Bias Multisensor Field Mosaic using MPE radar climatologies

Gauge-Only Field Analysis which is normalized to PRISM data in

the absence of radar precipitation estimates

Process 3, or “P3” Field (in later builds)

Local bias satellite precipitation estimates (in later builds)

Note: The average and maximum radar field mosaics which do not use bias correction or radar climatologies were added in later builds of MPE from old Stage III

2. More robust radar bias calculation scheme

3. Radar Height Coverage Field from the radar climatologies

4. Ability to edit final precipitation estimate by drawing polygons and inserting any other field or any precipitation value in it. Very quick removal of anomalous propagation.

5. Improved Gauge Table

There are three new fields in the Build 8.2 version. They are the mergedlocal bias satellite-local bias radar mosaic, merged local bias satellite-gauge mosaic, and the merged local bias satellite-gauge-local bias radar precipitation estimate mosaic (shown above). These fields fill in the gaps with local bias satellite estimates where both rain gauge data and radar data do not exist. So far the WGRFC HAS forecasters seem to think the satellite-gauge-radar combination has the most utility.

The Future – Looking at new Precipitation Fields.

MPE Lessons Learned

  • The WGRFC is now experimenting with NSSL’s Next Generation QPE (Q2) precipitation estimates in MPE.
  • Based on NSSL’s National Mosaic and Multi-sensor QPE (NMQ) project.
  • Uses the Hybrid Scan Reflectivity from the WSR-88D, thus does not have the limitations associated with the DPA files (automated “AP” removal, precipitation estimates beyond 230 km.).
  • NSSL Q2 algorithm adjusts the Z/R relationship “on the fly” for tropical systems or warm rain processes.
  • Certain fields work better in certain types of weather regimes than others
  • In thunderstorms, either the local bias multisensor field or the field bias multisensor field work best
  • In warm rain process or tropical rainfall events, the local bias multisensor field or the maximum radar mosaic work best
  • In stratiform rainfall events, radar has trouble picking up light rain, especially at great distances. Therefore, either the local bias multisensor mosaic or the Gauge-Only Analysis work best.
  • Local bias satellite precipitation estimates are used exclusively over Mexico.
  • Conclusion was reached that the local bias multisensor field mosaic should be our default field

Additional questions? Contact me at:

[email protected]

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