This is part two of a two-module series on estimation of observed precipitation. Through the use of rich illustrations, animations, and interactions, this module provides an introduction to the science behind successful application of the products and tools available through the NWS Multisensor Precipitation Estimator (MPE) software and related products. An overview is presented of the key fields available in MPE along with illustrations of their use. These include radar, gauge, satellite, bias-adjusted radar, and multisensor fields of precipitation accumulation along with data displays and tables used for evaluating and editing the data. Subsequently, methods for additional data editing with MPE's polygon editing tool are explained, as well as the selection of a best estimate. Finally, a case study section is offered to show how these methods have been applied in the field for a variety of events and locations.
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- Describe scientific applications of the Multisensor Precipitation Estimator (MPE) software.
- List some of the related precipitation estimation programs used within MPE.
- Differentiate between the different MPE precipitation fields: single-sensor, bias-adjusted, and multisensor.
- Describe the basic science used in bias-adjusted radar QPE.
- Explain the differences between local and field bias adjustments.
- Describe the basic scientific approach used in multisensor fields.
- Describe the advantages of multisensor QPE.
- Explain what MPE's best estimate QPE is.
- Describe how MPE gauge editing tools can be used to improve gauge analyses.
- Explain when a pseudo gauge might be used.
- Explain how polygon edits may be used to improve the utility of QPE.
- Explain how bias information, such as the local span, can be used to assess the representativeness of a bias adjustment factor.
- Differentiate between some of the primary QPE issues related to warm season, cool season, and non-precipitating echoes.
- Apply some case-specific lessons of generating QPE including: dealing with poor radar coverage, bright band, intense convection, warm rain, snow, and ground clutter.
Precipitation Measurement, Quantitative Precipitation Estimation, QPE, Remote Sensing of QPE, estimating observed precipitation, precipitation climatology, Multisensor Precipitation Estimator, MPE, Quantitative Precipitation Forecast, QPF, radar precipitation, satellite precipitation, rain gauge, precipitation analysis, precipitation analyses, Mountain Mapper?s Daily QC, National Mosaic and Multi-sensor QPE, NMQ, Q2, SNOTEL, NOHRSC, MPE editor, PrecipFields, Best Estimate QPE, Multisensor Mosaic, MPE Radar Mosaic, radar QPE, Satellite QPE, , Digital Precipitation Array, DPA, Hydrologic Rainfall Analysis Project, HRAP, grid bin, Average Radar Mosaic, Max Radar Mosaic, Raw Q2 Mosaic, Dynamic Z-R relationship, Z-R relationship, Precip Flag, Z-R coefficients, Warm rain, Gage Only Analysis, radius of influence, gauge report, gauge density, Local Bias Radar Mosaic, Local Bias Multisensor Mosaic, Multisensor Mosaic, Mean Field Bias, Field Bias Radar Mosaic, gauge-radar pair, Triangular Irregular Network, TIN, PRISM, PRISM-adjusted gauge analyses, Gages, Basefields, Polygons menu, gauge data evaluation, gauge data quality Control, Daily QC tool, pseudo gauges, pseudo gage, Daily QC, QC Precipitation, MPE Field Gen, MPE Editor, Gage Table, Display 7X7, Local Bias Display, Local Span, Local Bias, bias correction, Polygon edits, Blocked Radar Sector, Melting Layer, Bright Band, Anomalous Propagation, High Resolution Precipitation Estimator, HPE
July 2020 - The lesson was updated to current internet standards (mp4/html5), with no changes to content.
