Daily Mapping of 24 hr Rainfall at Pixel Scale over South Africa using Satellite, Radar and Raingauge Data Geoff Pegram 1, Izak Deyzel 2, Pieter Visser 2, Deon Terblanche 2, Scott Sinclair 1 & George Green 3 1.Civil engineering, University of KwaZulu-Natal, DURBAN, RSA 2.METSYS, South African Weather Services, BETHLEHEM, RSA 3.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

Objectives South 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 Estimation A: 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 IRIR & WVIR & VISIR, WV & VIS Initial Screen - WV-IR<-3° - -Sun Angle > Th’ld Texture IR > 180°KVIS > 180°K ASM > 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: R c = 0.45(230-IR) Medium: R m = (267-IR) 1.85 Warm Stratiform: R w =[{alog( IR)/2000] 0.625

Rain-Rate Estimation algorithm COLD CONVECTIVE < 218K MIDDLE LAYER K WARM CLOUDS K Coastal NO DEEP CONVECTIVE ACTIVITY ADAPTED IR POWER LAW RAIN- RATE 24-hr MSRR - R s Sufficient slope Z-R derived HALF-HOURLY MSRR FIELD - R hs R hs ACCUMULATION Recursive speckle filter Image smoothing filter Rhms 0 Rhws Rhcs 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.

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 Network superimposed on the Mean Annual Rainfall map Radar range is an (ambitious!) 200km 1300 km N-S 1600 km E-W Area 1.2 Mkm 2

Automatically reporting raingauges 290 ± some outside RSA via HYCOS

Kriged Gauge Explained Variance Field - V G

24-h Accum’d Kriged Gauge Field - G K

Radar Explained Variance Field - V R Over- ambitious estimation of radar accuracy with range Needs revision Note FFT wrapping

24-h Accum’d Kriged Radar Field - R K

Merged 24h Radar/Gauge Rainfall Field: R|G = (R K *V R +G K *V G )/(V R +V G )

Best Estimate: 24h Satellite Rainfield - S R

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

Merged Satellite|Gauge Rainfall Field: S|G = S R – S Z + G K

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

Final Merged Rainfall Field: R|G,R,S = { R|G*(V R orV G )+ S|G *S B }/{(V R orV G )+S B }

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)

(c) The rain-gauge observation are Kriged to obtain the best linear unbiased estimate of rainfall on the radar grid - M G (d) The radar pixel values at the rain-gauge positions are Kriged onto the remainder of the grid to give a mean field - M R (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-M R +M G

Correlated FieldContaminated Field Kriged FieldMerged Field Explained variance weighting

Correlated FieldContaminated Field Kriged FieldMerged Field Conditional merging

Comparison of Explained Variance and Conditional Merging i) Generate 1000 independent rainfall fields using the “String of Beads” Model (Pegram and Clothier, 2001) 1000

ii) Add bias and noise to simulate radar measurements of the “true” rainfall field Bias + Noise iii) Sample the “true” rainfall field to get a set of unbiased rain- gauge observations Rain-gauge locations

iv) Krige the rain-gauge observations onto the unobserved radar pixels Kriging v) Apply Conditional merging procedure

Evaluation of comparison vi) Compute the mean error at each pixel over the 1000 realizations vii) Compute the variance of the errors at each pixel over the 1000 realizations

Simulation experiment

And finally a real cross-validation field experiment Compare straight Kriging and Conditional Merging on 40 raingauges on a 4600 km 2 catchment Use cross-validation – estimation of daily total at each gauge separately using the remaining data

Layout of the Liebenbergsvlei gauge network 10 km

Comparison of daily mean cross- validation errors

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

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