Witold Krajewski
IIHR Hydroscience and Engineering, University of Iowa
CIROH_HydroLearn
Introduction to Radar Hydrology
Problem Statement
Almost every hydrologic decision a forecaster makes, from a flash-flood warning to a river forecast to a drought update, starts with one deceptively simple question: how much rain actually fell, and where? The answer is never measured directly over a whole basin. It is estimated, by stitching together weather radar, rain gauges, and satellite, each of which sees precipitation through its own distortions.
A radar does not measure rain; it measures the energy scattered back by raindrops and then converts that to a rate through an assumed relationship. A rain gauge gives a real measurement, but only for a funnel a few inches wide, and even that funnel under-catches in wind. Satellite sees everywhere but resolves the least. Modern operations fuse all three into a single national grid, MRMS, updated every two minutes at 1 km. Knowing where that grid is trustworthy, and where it is not, is core hydrology.
Module Overview
This module gives early-career NWS hydrologists a compact, hands-on grounding in Quantitative Precipitation Estimation (QPE): what the instruments really measure, why radar coverage makes or breaks an estimate over a basin, and how to pull, read, and compare today's operational MRMS products for a real flood event.
The hands-on activities use cloud-hosted MRMS data and one running case study, the 4-7 July 2025 Central Texas floods (Kerr County / Guadalupe River), runnable directly in Python / Google Colab.
Topics Covered
Quantitative Precipitation Estimation (QPE); radar reflectivity and the Z-R relationship; drop-size distribution; dual-polarization variables (ZDR, KDP, ρHV); rain-gauge under-catch and representativeness; satellite QPE (IR, passive microwave, SCaMPR, GPM); radar beam geometry and coverage; radar quality and climatology; the Multi-Radar Multi-Sensor (MRMS) system; radar-only vs. gauge-corrected / multisensor QPE; accessing MRMS data from the cloud.
Prerequisites
Before starting this module, learners should have:
- Basic meteorology and hydrology vocabulary (precipitation, reflectivity, stratiform vs. convective, accumulation).
- Comfort reading a map, a radar image, and a simple time series.
- For the Section 4 activities: enough Python to run a Jupyter notebook end-to-end (numpy, pandas, matplotlib). No prior MRMS experience is assumed.
Learning Objectives
At the end of this module, learners will be able to:
- Explain why "observed" precipitation is still an estimate, and state the trade-offs of radar, gauge, and satellite QPE.
- Convert reflectivity to a rain rate with a Z-R relationship, and explain why a single relationship cannot be right everywhere.
- Describe how dual-polarization and gauge correction reduce QPE error.
- Judge, from beam height and terrain, whether radar coverage over a given basin is trustworthy.
- Access MRMS data from the cloud and compare radar-only against gauge-corrected / multisensor QPE for a real flood event.
This is accomplished through short readings on the fundamentals, a hands-on Z-R learning activity, and a sequence of MRMS data notebooks built on a single flood case study.
Suggested Implementation
This module is broken down into four sections with small units. Each section is self-contained and can be exercised individually. Total estimated effort is about 2 hours, self paced.
| Section | Estimated time |
|---|---|
| Section 1, Introduction | 5 min |
| Section 2, The basics of radar QPE | 35 to 40 min (includes Learning Activity 1) |
| Section 3, Radar coverage for hydrology | 25 to 30 min |
| Section 4, Radar QPE in practice (MRMS) | 40 to 45 min (includes the notebooks) |
Course Authors
Mohamed Abdelkader
IIHR Hydroscience and Engineering, University of Iowa
mohamed-abdelkader@uiowa.edu
Target Audience
Early-career hydrologists and operational forecasters at National Weather Service Weather Forecast Offices (WFOs) and River Forecast Centers (RFCs) who want to use radar-based QPE confidently and know its limits.
Tools Needed
A computer with internet access and a modern browser. For the hands-on activities, a Python environment (numpy, pandas, matplotlib, xarray, cfgrib) or a Google account to run the notebooks in Google Colab. The MRMS data are open and cloud-hosted (AWS NODD and the Iowa State archive), so no special access is required.
Expected Effort
About 2 hours total. Self paced.
Course Sharing and Adaptation
This course is available for export by clicking the "Export Link" at the top right of this page. You will need a HydroLearn instructor studio account to do this. You will first need to sign up for a hydrolearn.org account, then register as an instructor by clicking 'studio.hydrolearn' and requesting course creation permissions.
Recommended Citation
Krajewski, W., & Abdelkader, M. (2026). Introduction to Radar Hydrology: Quantitative Precipitation Estimates. University of Iowa.
Acknowledgments
Section 2 is adapted from the COMET® Precipitation Estimates, Part 1: Measurement module (UCAR/MetEd), supplemented with standard radar-meteorology references (Marshall & Palmer 1948; Fulton et al. 1998; Ryzhkov et al. 2005; Zhang et al. 2016) and the WMO solid-precipitation gauge intercomparison (Goodison et al. 1998). The MRMS hands-on activities are built on the MRMS QPE notebook series, adapted from the Project Pythia MRMS Cookbook. Operational product details follow the NOAA/NWS Warning Decision Training Division (WDTD) MRMS Products Guide.