# Presentation: 150904

## Detection of Heavy Precipitation

- OHD’s Research and Development in Radar and Multisensor Applications
- David Kitzmiller
- Hydrologic Science and Modeling Branch
- Hydrology Laboratory
- Office of Hydrologic Development
- February 1, 2006

## Topics

- Potential for improving detection of heavy rainfall through use of radar mosaics
- Implications of spatial resolution impacts on radar-rain gauge correlation
- Probabilistic radar rainfall estimates and potential applications in Flash Flood Monitoring and Prediction
- Quantitative nowcasts of rainfall

## Hydrometeorology Group

- Feng Ding
- Richard Fulton
- Shucai Guan
- David Kitzmiller
- Chandra Kondragunta
- Dennis Miller
- Kiran Shrestha

## Collaborating Partners

- University of Iowa Department of Civil and Environmental Engineering (Krajewski, Ciach, Villarini)
- National Severe Storms Laboratory
- WISH
- RRAD
- Princeton University Department of Civil and Environmental Engineering (Jim Smith)
- RS Information Systems, Inc. (McLean VA)

## Acronyms...

- DHR: Digital Hybrid Reflectivity (also precipitation accumulations from DHR)
- DPA: Digital Precipitation Array (one-hour radar rainfall accumulations)
- FFG: Flash Flood Guidance (amount of rainfall required to cause small streams to flood)
- FFMP: Flash Flood Monitoring and Prediction System (Part of SCAN in AWIPS)
- Z-R: Reflectivity-to-rainrate model used to estimate rainfall from radar measurements
- Bias:  (  Gauge Rainfall) / ( Radar Rainfall) for many collocated individual 1-h estimates

## Typical One-Hour Rainfall Product:

## Radar-to-Raingauge Comparisons Are Often Discouraging...

## Range Influence on Radar Detectionof Rainfall

- Detection efficiency decreases with range
- Radar detects hydrometeor distribution that is different from that at surface
- Horizontal advection of precipitation affects apparent radar-gauge agreement
- Pointing accuracy of radar
- Location errors for gauges

**Most of these errors are magnified**
** ****at longer ranges**

## AWIPS Multisensor Precipitation Estimator Mosaicking Technique

- Height Field

- Radar Coverage Field

**AWIPS Multisensor Precipitation Estimator Mosaicking Technique**

## Detection of Heavy Rain4-km Mosaic vs. 1-km Single-Site Estimates

- Created a set of matching estimates:
- Rain gauge
- Single-radar (based on Digital Hybrid Reflectivity products used in FFMP)
- Multisensor Precipitation Estimator mosaic
- Sites:
- KPBZ (Pittsburgh)
- KLWX (Sterling)
- KFCX (Blacksburg)
- 2004 Warm season, 7 rain events
- Result: mosaic field has higher correlation with gauge estimates than does single-radar

## Probability of Detection of 1-h rainfall ≥12.5 mm by single radar (DHR) vs. mosaic

## False alarms  for ≥12.5 mm 1-h rainfallby single radar (DHR) vs. mosaic

## Correlation Between Radar Estimates and1-h, 12.5-mm Rainfall Events

## Effects of Spatial Smoothing onRadar Rainfall Estimates

- We found that some degree of spatial smoothing generally improves radar-rain gauge correlations
- There are several potential sources of radar/gauge location disagreement:
- Pointing accuracy of radar
- Horizontal advection of raindrops

## travel 1 nm horizontally, or two WSR-88D range gates

**Raindrops detected at 60 nm range are **
**about 1 nm above ground (assuming level **
**surface)**
**Terminal velocity of large drops ~ 20 knots**
**Travel time to ground ~ 3 minutes**

**If mean wind is 20 knots, raindrops can**
**travel 1 nm horizontally, or ****two WSR-88D range gates**

**Homogeneity of rainfall field mitigates advection effects,**
**but advection might play large role in poor radar/gauge **
**correlations in light, spotty rain**

## Effects of Spatial Smoothing of Radar Estimates on Radar/Gauge Correlation

## Effects of Spatial Smoothing of Radar Estimates on Radar/Gauge RMS Error

## Effects of Spatial Smoothing On Areal Rainfall Estimates

- Quality of areal rainfall estimates based on 1km x 1 DHR products and 4km x 4km DPA assessed by comparing with collections of rain gauges in ARS Oklahoma micronet
- Quality of DHR-based and DPA-based areal estimates is nearly identical
- The issue warrants further investigation for confirmation
- What horizontal resolution is optimum for FFMP?

## Probabilistic Relationships Between Radar and Rain Gauge Estimates

- Most common forms of bias correction are based on long-term collections of 1-h radar/gauge paired observations
- Actual bias between radar estimates based on Z-R and rain gauges depends on magnitude of the rainfall rate
- A common method of selecting radar rainfall alert thresholds (fraction of critical ground truth value) is not statistically reliable

## Flash Flood Guidance

- An estimate of the rainfall required to cause small headwater streams to reach bankfull
- Commonly expressed as 1-h, 3-h, 6-h amounts
- Routinely produced by River Forecast Centers based on soil type, antecedent rainfall

## 1-Hour FFG from MARFC, OHRFC

## Common Operational Strategy

- Take action when radar rainfall estimate is 80% of FFG value
- Closer examination of basin rainfall history
- Call for spotter reports
- However, threat of actual rainfall exceeding FFG is strongly dependent on the radar estimate itself

## Probability of Gauge Rainfall ≥ 120% of Radar Estimate

- Data from KTLX, KINX, KSRX, 2004-2005 warm seasons

## Probability of Gauge Rainfall ≥ 120% of Radar Estimate

- Probability of exceeding a given gauge/radar ratio decreases with radar rainrate
- For a radar estimate of 0.4 inch, there is a 45% chance that rainfall will exceed 0.5 inch
- For a radar estimate of 1.5 inches, there is only a 15% chance that rainfall will exceed 1.8 inches

## Probabilistic Relationships Between Radar and Rain Gauge Estimates

- Work carried out at University of Iowa (Krajewski, Ciach, Villarini) shows that radar rainfall errors can be modeled with a set of power-law functions
- Results confirmed on a larger data sample by OHD

## After correcting radar estimates for overall long-term bias:

- Radar underestimates lighter amounts and overestimates higher amounts
- A simple power law relates expected rainfall to initial Z-R estimate

## After Correcting Radar Estimates For Long-Term

- 1-h Radar Rainfall Estimate, bias corrected, mm
- KTLX, 1996-2003

- From Krajewski and Ciach, 2005

- Bias, a Magnitude-Dependent Bias Remains...

## Standard Deviation of The Radar Estimate Error

- 1-h Radar Rainfall Estimate, bias corrected, mm
- KTLX, 1996-2003

- (Spread of Estimates) Can Also Be Modeled As A
- Power-Law Function:

## After correcting radar estimates for overall long-term bias:

- Rainrate-dependent bias is approximated by a power-law curve
- Standard deviation of multiplicative error is also a power-law curve
- Distribution of multiplicative errors for any given radar estimate is approximately normal
- Formulation of probability of rainfall exceeding a critical value:

## B is long-term gauge/radar bias

- a,b are parameters of bias power law;
- c,d,e are parameters of standard deviation power law;
- RR is initial radar estimate;
- THRES is FFG or other critical rain amount

**Formulation of Probability of Rainfall (RR)**
**Exceeding a Critical Value THRES**

## Application of Probabilistic QPE:

- Power-law parameters are determined from extended gauge/radar sample
- Parameters have seasonal and site dependence
- Probability equation could be incorporated as new option in FFMP

## Multisensor Precipitation Nowcaster (MPN)

- Extrapolative forecast model for 0-1 hour rainfall amounts
- Based on mosaicked radar and/or radar/gauge rainrate field
- Produces forecasts of 1-h, 3-h, 6-h rainfall amounts ending 1 hour in the future
- Advantages over operational SCAN 0-1 hour extrapolative algorithm:
- Radar mosaic input, rather than single radar
- Incorporates real-time radar/gauge bias information

## Scores For MPN, Persistence Forecasts:Detection of > 15mm, 1-Hour Rainfall, 4-km Resolution 

- 20 Flash Flood Cases

## Potential Applicationsof MPN

- Direct application in FFMP
- Precipitation output serves as input to hydrologic models (distributed, Site Specific)
- Reed et al. reporting results using MPN output in distributed hydrologic modeling at AMS Hydrology Conference this week

## Observed Hydrograph

- Model Hydrograph with QPF

- Modeled Hydrograph,
- Assuming 0 QPF

- Modeled Hydrograph,
- Assuming rain persistence

## Summary

- In many parts of the U.S. the radar network is dense enough that mosaics of rainfall estimates could provide significantly better detection of rainfall than single-radar estimates
- Use of prior knowledge of error statistics, through a probabilistic model, could simplify warning decisions.
- Quantitative nowcasts of rainfall can improve forecasting of flood events through application in either FFG or physical hydrologic models
- The optimum degree of spatial smoothing for radar rainfall estimates needs further investigation

## Questions Raised:

- How to integrate multisensor mosaics into flash flood operations?
- Enhancement of Multisensor Precipitation Estimator in AWIPS
- Ongoing investigation of Q2 algorithm originated at NSSL
- Using probabilistic formulation of radar/rain gauge error in FFMP
- Determining optimum spatial resolution of radar rainfall estimates

## OSIP/HOSIP Status:

- Providing radar mosaic estimates for flash flood detection (MDL, NSSL, OHD)
- OSIP Stage 1
- Multisensor Precipitation Nowcaster:
- HOSIP Stage 2
- Radar Probabilistic Precipitation Estimates:
- HOSIP Stage 2
- Distributed hydrologic models for flash flooding
- HOSIP Stage 3

## Related OHD Projects

- Distributed hydrologic modeling for flash flooding (Hydrology Group)
- Precipitation estimates from Terminal Doppler Radar (NEXRAD Software Project)
- Evaluation of NSSL dual-pol precip algorithm
- Automated merging of rain gauge / radar / satellite estimates in AWIPS (Hydrometeorology Group)
- Automated QC for rain gauge data (Hydrometeorology Group)
- Baltimore Flash Flood Forecast Project (Princeton University, USGS, Hydrometeorolgy Group)

## Questions?

## 3-Hour FFG from MARFC, OHRFC

## Slide 42

## Expected 1-Hour Rain Gauge Value

- As Function of Radar Estimate

- Radar estimates
- adjusted for bias
- (gauge/radar):
- KTLX bias: 0.7
- KLWX bias: 1.07
- KFFC bias: 0.93
- May-Sep 2004