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

Theory / Schematic Algorithm Inputs InstrumentPERSIANN TRMM 2A12 (TMI)TSDIS DMSP SSM/INESDIS AMSRNESDIS AMSUNESDIS IRNOAA/CPC (4 km geo- IR) Gauge Analysis–

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 x70 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 5x5 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 The output is 0.25 o x 0.25 o, 30-minutes rain rate Operational PERSINAN provide data around 2-day delay Spatial-temporal Integration: 0.25 o, Hourly Rainfall window (5x5) IR-T b at 0.25 o x0.25 o Res. from Geostationary Satellites 30-minute Rainfall Rate Collect MW RR within 30 minutes period: TRMM TMI 2A-12 & AMSR, AMSU, SSM/I Rain Rate (NESDIS) Matching Error of Rainfall Estimates in 30 minutes OUTPUT INPUT Spatial-temporal Integration: 1 o x1 o Daily Rainfall etc… Spatial-temporal Integration: 0.25 o, 30 Minutes Rain Rate PERSIANN Parameter Adjustment

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 0.25°x0.25° Spatial Coverage 50°N-S (60° possible) Update Frequency 1-hr Data Latency 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