Daily Mapping of 24 hr Rainfall at Pixel Scale over South Africa using

Satellite, Radar and Raingauge Data

Geoff Pegram1, Izak Deyzel2, Pieter Visser2, Deon Terblanche2, Scott Sinclair1 &

George Green3

1. Civil engineering, University of KwaZulu-Natal, DURBAN, RSA2. METSYS, South African Weather Services, BETHLEHEM, RSA3. Deputy Director, Water Research Commission, PRETORIA,

RSA

Presentation Overview

• Objectives• Satellite information as a data source

– Producing a satellite rainfall map for South Africa– Validation of Satellite Rainfall Fields

• Use Gauge and Radar information to augment• Producing the Merged Rainfall Field• Verification of the Merged Rainfall Fields• How do we improve the product?• A spin-off is the ground-truthing of satellite data

ObjectivesSouth Africa has limited resources:

– Sparse raingauge network– Patchy C-band radar coverage (non-Doppler)

SO … • Use Satellite data to derive a Daily Rainfall Field

over South Africa at highest spatial resolution• Combine rainfall estimates from METEOSAT,

Rain Gauges and Radar to produce a Spatially Interpolated Mapping of Rainfall (SIMAR) over South Africa

• Constantly seek ways to improve these estimates

Producing a Satellite Rainfall Map for South Africa

• Overview of the Multi-Spectral Rain-Rate (MSRR) technique

• Flow diagram of the MSRR algorithm layout

• Components of the MSRR algorithm, particularly classification by texture

METEOSAT data used in SIMAR

++ ++

==

VIS

WV IR

Multi-Spectral Rain-Rate EstimationA: Mask out non-raining information

– Cirrus, sun, speckles– Separate topography – cold versus warm coastal rain

• When available, use VIS, WV & IR data to define mask

• Use texture analysis to identify potential rain• Use image processing techniques: median filtering

and edge detection to sharpen mask and clean up

B: Use IR to estimate cold, intermediate and warm (coastal) rain

Infrared and Water Vapor Spectral Difference Cloud Mask

• Negative Infrared and Water Vapor spectral difference field• Spatial Correspondence to strong Radar echoes• Mask =1 for deep moist cold cloud areas

Exploit Texture to Improve Estimate

• Compute the Grey-Level Co-occurrence matrix (GLCM) at every point in the field

• Thence compute the Angular Second Moment (ASM) at every point in the field

• Defines a Mask that yields improved Accumulated Rainfall Estimates – comparable to TRMM estimates

• Mask using WV when available, else IR

Texture Analysis of Infrared & Visible images

• Grey Level Co-occurrence Matrix (GLCM) texture features• Correspondence between certain texture features of Infrared or Visible

cloud images to moderate Radar echoes

IR VIS

Discriminant Function based on LDA delineates rainfall areas

• Linear Discriminant Analysis (LDA), trained on Radar data, delineates possible rainfall areas

Masked VIS Radar

Flow diagram of the MSRR algorithm

Data available

IR IR & WV IR & VIS IR, WV & VIS

Initial Screen

- WV-IR<-3° - WV-IR<-3°

- Sun Angle > Th’ld

Texture

IR > 180°K VIS > 180°KASM > 1.50(115+IR) ASM > 6.08(VIS-42)

- QC on IR & VIS

Filter IR < 273°K

WR < 253°K

VIS > 42 (albedo)

WV & VIS warm

WAR Speckle Filter > 33%

IR mask: pass 1 – Go To Rain-Rate Estimation

The IR → Rain-rate Relationship

Cool: Rc= 0.45(230-IR)

Medium: Rm= 0.00303(267-IR)1.85

Warm Stratiform: Rw=[{alog(73.32-0.173.IR)/2000] 0.625

Rain-Rate Estimation algorithmCOLD CONVECTIVE < 218K MIDDLE LAYER 219-267K WARM CLOUDS 268-278K

Coastal NO

NODEEP

CONVECTIVE ACTIVITY

ADAPTED IR POWER LAW RAIN-

RATE

24-hr MSRR - Rs

Sufficient slope

Z-R derived

HALF-HOURLY MSRR FIELD - Rhs

Rhs ACCUMULATION

Recursive speckle filter

Image smoothing filter

Rhms 0RhwsRhcs

Rh*s = Half-hourly satellite rainfield

Improvement of Daily Satellite Rainfall Fields

IR masked field & Final rainfield estimate• improve the vast spatial and quantitative overestimation of rainfall

fields due to Cirrus contamination• improve the estimated spatial structure of daily rainfall fields• improve the detection of warm rain conditions, using algorithms not

specifically designed for convective rain systems.

Preliminary Validation

• validated with 300 1x1 min gridded raingauge values from Radar interpolated raingauge fields.

Probability of Detection

0 0.2 0.4 0.6 0.8 1

20020717

20020718

20020719

20020721

20020815

20020816

20020817

20020818

Skill Score

0 0.2 0.4 0.6 0.8 1

20020717

20020718

20020719

20020721

20020815

20020816

20020817

20020818

Field Bias

-8 -7 -6 -5 -4 -3 -2 -1 0 1 2

20020717

2002071820020719

20020721

20020815

2002081620020817

20020818

mm

False Alarm Ratio

0 0.2 0.4 0.6 0.8 1

20020717

20020718

20020719

20020721

20020815

20020816

20020817

20020818

Verification of Satellite Daily Rainfall Fields

• Generally overestimated• Warm rain needs adjustment

Coastal < 1000m

METEOSAT-7 Data availability

Producing the SIMAR Merged Daily Rainfall Field

• Why merge the rainfall fields ?

• Characteristics of each data source

• Explain the merging techniques

• Discuss the operational implementation of the merging routines

The steps in making a SIMAR map

• Collect 24-hour rainfall data (up to 8:00 am)• Clean 5-minute radar-rainfall images and

accumulate into a 24-hour mosaic• Process available satellite images of IR, WV &

VIS from METOSAT 7 to get 24-hour estimate of rainfall over RSA

• Combine: Gauge-Radar, Gauge-Satellite estimates

• Post the map on the web by 11:00 am

Availability of Ground-based Rainfall Sensors

• Weather radars: 11 C-band – except one S-band

• Rain-gauges: 290 ± daily reporting climatological stations

South African Radar Networksuperimposed on the Mean Annual Rainfall map

Radar range is an (ambitious!) 200km

1300 km N-S

1600 km E-W

Area 1.2 Mkm2

Automatically reporting raingauges 290 ±some outside RSA via HYCOS

Kriged Gauge Explained Variance Field - VG

24-h Accum’d Kriged Gauge Field - GK

Radar Explained Variance Field - VR

Over-ambitious estimation of radar accuracy with range

Needs revision

Note FFT wrapping

24-h Accum’d Kriged Radar Field - RK

Merged 24h Radar/Gauge Rainfall Field:R|G = (RK*VR+GK*VG)/(VR+VG)

Best Estimate: 24h Satellite Rainfield - SR

Mean Satellite Field smoothed from Satellite Estimates at Gauge locations by Splines - SZ

Merged Satellite|Gauge Rainfall Field:S|G = SR – SZ + GK

Satellite Bias Skill Score Field – SB: compare S|G with R|G in 9x9 blocks at gauges – interpolate

with Splines

Final Merged Rainfall Field:R|G,R,S = {R|G*(VRorVG)+ S|G *SB}/{(VRorVG)+SB}

SIMAR

Part of the introductory SIMAR web-page

Available daily by 11:00 am with previous day’s rainfall maps

Verification of Merged Daily Rainfall Fields

How do we improve this?

• Refine the merging of radar with gauge data to obtain better ground-truthing fields

An alternative method to improve SIMAR?

• The explained variance method tried to be “fair” to gauges and radar

• If we believe the gauges, then we want to condition the radar field onto the gauge readings as we did with the satellite images to get the S|G fields

• We call it “Conditional Merging”

Description of the Conditional Merging technique

(a) The rainfall field is sparsely observed on a regular grid at rain-gauge locations

(b) The rainfall field is also observed by radar on the regular grid - RR

Adapted from Ehret (2002)

Adapted from Ehret (2002)

(c) The rain-gauge observation are Kriged to obtain the best linear unbiased estimate of rainfall on the radar grid - MG

(d) The radar pixel values at the rain-gauge positions are Kriged onto the remainder of the grid to give a mean field - MR

(e) A rainfall field that coincides with the rain-gauge readings, while preserving the mean field deviations of the radar field is obtained as RR-MR+MG

Correlated Field Contaminated Field

Kriged Field Merged Field

Explained variance weighting

Correlated Field Contaminated Field

Kriged Field Merged Field

Conditional merging

And finally a real cross-validation field experiment

• Compare straight Kriging and Conditional Merging on 40 raingauges on a 4600 km2

catchment

• Use cross-validation – estimation of daily total at each gauge separately using the remaining data

ð

d d d

d d d d d

d d d d d

d d d d

d d d d

d d d d

d d d d

d d d d

d d

d

d d

d d d

d d

d

d

BETHLEHEM

MRL5

Layout of the Liebenbergsvlei gauge network

10 km

Comparison of daily mean cross-validation errors

-10 0 10 20 30 40 50 60

96/01/24

96/01/25

96/01/27

96/02/01

96/02/05

96/02/11

Radar Error Mean Kriged Error Mean Merged Error Mean

mm/day

Summary

• We have made a start

• Our Department of Water Affairs trusts the SIMAR fields enough to routinely use them in their Flood Forecasting Division

• Ongoing improvements are being made

fin