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PERSIANN: Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks

PERSIANN is an algorithm that uses artificial neural networks to estimate precipitation from remotely sensed information. It operates at a spatial resolution of 0.25°x0.25° and provides rainfall rate at 30-minute intervals. The algorithm is capable of adjusting parameters based on concurrent microwave rainfall data to improve accuracy. The current version of PERSIANN is operational with a data latency of 2 days.

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PERSIANN: Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks

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

  2. Theory / Schematic • Algorithm Inputs

  3. Theory / Schematic (cont.) • IR Calibration Data Cube • The data coverage area (60oS—60oN) is separated into a number of 15o x70o lat-long subregions, with partial overlapping of 5o in each subregion • Rainfall rate is calculated at 0.25o 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/0.25o matched MW and IR pixels were collected. • Rainfall rate in the classified IR feature group is temporal adjusted at each 30 minutes period

  4. INPUT OUTPUT Collect MW RR within 30 minutes period: TRMM TMI 2A-12 & AMSR, AMSU, SSM/I Rain Rate (NESDIS) window (5x5) IR-Tb at 0.25ox0.25o Res. from Geostationary Satellites Spatial-temporal Integration: 0.25o, 30 Minutes Rain Rate Spatial-temporal Integration: 0.25o, Hourly Rainfall PERSIANN Matching Error of Rainfall Estimates in 30 minutes Parameter Adjustment Spatial-temporal Integration: 1ox1o Daily Rainfall etc… 30-minute Rainfall Rate 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 0.25ox 0.25o, 30-minutes rain rate • Operational PERSINAN provide data around 2-day delay

  5. Theory / Schematic (cont.) • Strengths and Weaknesses of Underlying Assumptions • Generating hourly rainfall rate at resolution of 0.25o • 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

  6. 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 0.04o 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

  7. 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

  8. 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

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