Precipitation Estimation from Remotely Sensed Information using Artificial

Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Kuolin Hsu, Yang Hong, Dan Braithwaite, Xiaogang Gao, and Sorooshian UC Irvine

Theory / Schematic • Algorithm Inputs Instrument TRMM 2 A 12 (TMI) PERSIANN TSDIS DMSP SSM/I NESDIS AMSR NESDIS AMSU NESDIS IR Gauge Analysis NOAA/CPC (4 km geo. IR) –

Theory / Schematic (cont. ) • IR Calibration Data Cube • The data coverage area (60 o. S— 60 o. N) is separated into a number of 15 o x 70 o lat-long subregions, with partial overlapping of 5 o in each subregion • Rainfall rate is calculated at 0. 25 o and 30 minutes spatial-temporal scale • IR textures, in terms of mean and standard deviation of longwawe IR brightness temperature within 5 x 5 neighboring pixels, were collected • Within each subregion, 30 -minute/0. 25 o matched MW and IR pixels were collected. • Rainfall rate in the classified IR feature group is temporal adjusted at each 30 minutes period

Theory / Schematic (cont. ) • Algorithm Process • The current PERSIANN is operated to generate rainfall rate at every 30 minutes • parameters of PERSIANN is adaptively adjusted every 30 minute period when concurrent MW RR from TRMM and other (DMSP & NOAA) satellites are available • • INPUT window (5 x 5) IR-Tb at 0. 25 ox 0. 25 o Res. from Geostationary Satellites Collect MW RR within 30 minutes period: TRMM TMI 2 A-12 & AMSR, AMSU, SSM/I Rain Rate (NESDIS) PERSIANN Parameter Adjustment Matching Error of Rainfall Estimates in 30 minutes The output is 0. 25 ox 0. 25 o, 30 -minutes rain rate Operational PERSINAN provide data around 2 -day delay 30 -minute Rainfall Rate OUTPUT Spatial-temporal Integration: o 0. 25 , 30 Minutes Rain Rate Spatial-temporal Integration: o 0. 25 , Hourly Rainfall Spatial-temporal Integration: o o 1 x 1 Daily Rainfall etc…

Theory / Schematic (cont. ) • Strengths and Weaknesses of Underlying Assumptions • Generating hourly rainfall rate at resolution of 0. 25 o • • Available for accumulating the hourly rainfall to 6 -hour, daily, monthly scales Capable of providing diurnal rainfall pattern over the study region • All MW rainfall rates are used to the adjustment of IR-RR parameters at every 30 -minute period • A small step size adjustment of the fitting function based on the current MW rainfall data • Heavily relied on the accuracy of MW-based rainfall provided by NESDIS • Tend to underestimate high rainfall intensity • Need to evaluate precipitation over the mountain and high latitude region

Theory / Schematic (cont. ) • Planned Modifications / Improvements Current • Evaluate PERSAINN rainfall with gauge estimates • Evaluate PERSIANN rainfall over the high latitude region • Operate PERSIANN-CCS (IR patch-based algorithm) to cover North America at resolution of 0. 04 o hourly scale • Adjust PERSIANN estimates based on GPCC gauge data to produce merged historical data set Short-term • Evaluate and provide the uncertainty of PERSIANN estimates • Provide seasonal near-global diurnal rainfall pattern • Operate PERSIANN-CCS to near global coverage Long-term • Integrate satellite information, local meteorological variables of regional atmospheric models, and topographical factors to classification of weather pattern and to the rainfall mapping

Algorithm Output Information • • Spatial Resolution Spatial Coverage Update Frequency Data Latency 0. 25°x 0. 25° 50°N-S (60° possible) 1 -hr 2 days delay operate at NESDIS in near-real-time is on going

Algorithm Output Information (cont. ) • Capability of Producing Retrospective Data (data and resources required / available) • • Currently 3/2000 -present Could go back to 1/98 with current data sets