COMPARING HRPP PRODUCTS OVER LARGE SPACE AND TIME SCALES Wesley Berg Department of Atmospheric Science Colorado State University PEHRPP Activities 1.Regional comparisons 2.High time resolution comparisons 3.Very high quality field programs 4."Big picture" comparisons Catch any artifacts not noticed in detailed statistics of above suites obvious systematic changes on a latitude line, related to availability of certain data types changes in time series, related to data availability Validation of large-scale quantities and characteristics against bulk quantities, existing products (GPCP, CMAP, etc.), streamflow data sets, water budgets, and subjective judgment Focus on thousands of kilometers and monthly time scales Goals of PEHRPP Project Characterize errors in various high resolution precipitation products (HRPP) on many spatial and temporal scales, over varying surfaces and climatic regimes Enable developers of HRPP to improve their products and potential users to understand the relevant characteristics of the products Define data requirements and computing resources needed for retrospective processing of HRPP Comparing Monthly-Averaged HRPP Online Comparing Monthly-Averaged HRPP Online Comparing Monthly-Averaged HRPP Online Time Series of Monthly-Averaged HRPP Image Comparisons of Monthly-Averaged HRPP Difference Images of Monthly-Averaged HRPP Scatter Plots of Monthly-Averaged HRPP Scatter Density Plots of Monthly-Averaged HRPP What Causes these Differences? Errors in the source data HRPP rely heavily on rainfall estimates from passive microwave sensors The amount/types of passive microwave data vary between techniques (i.e. some use SSM/I only while others use a combination of data from sensors of various resolution/quality) HRPP use different microwave retrieval algorithms, which often exhibit significant regional and/or time-dependent differences Calibration issues between data sources can lead to differences between various techniques (e.g. SSM/I TDR vs RSS, AMSR-E L1B vs. L2A). Errors due to method and/or assumptions used to combine data Differences in method used to compute/adjust IR estimates (e.g. intensity based on temperature or interpolated microwave estimates etc.) Assumptions regarding quality of microwave data may vary Algorithm Differences Regional Differences between TRMM TMI and PR TRMM Tropical Mean Rainfall Anomalies Version 5 vs. Version 6 Rainfall Detection Errors February 1, 2000 Differences in SSM/I Rainfall Time Series Resulting from Orbital/Calibration Differences Passive Microwave Calibration Issues Diurnal variability Channel differences (e.g. changes between SSM/I, TMI, and AMSR-E) Resolution differences (beam filling effect) Radiative transfer model errors Quality control issues Cross-track biases Brightness temperature dependence Passive Microwave Calibration Differences AMSR-E L1B vs. L2A TMI * AMSR-E SSM/I * SSMIS * ** /-1, 3, 7** 53.1 WINDSAT *** 18.7*** ***49.9 Sensor Inc Ang Currently Available Passive Microwave Rainfall Sensors Diurnal Impacts Change is SSM/I Observation Times For DMSP F13, F14, and F15 Summary There are significant regional and time-dependent differences between rainfall estimates from current passive microwave algorithms, which arise from assumptions in the retrieval algorithms. Due to the non-parametric nature of current passive microwave retrieval algorithms, the application of a given algorithm to sensors with differing characteristics may also lead to significant biases. Differences in calibration between satellites/sensors can lead to significant differences in rainfall estimates (.e.g. 20% differences in tropical mean rainfall between TDR and L1C SSM/I data) Significant unresolved differences remain between SSM/I rainfall estimates from F13, F14, and F15, which is due to a combination of physical differences (e.g. diurnal cycle) and calibration differences. Level 1C project is a GPM prototype system designed to address inconsistencies between data sources and to enhance usability for algorithm developers (Details provided in afternoon talk by Kummerow).