87th AMS Annual Meeting

Thursday, 18 January 2007: 11:45 AM
Orographic Enhancements in Precipitation: Construction of a Global Monthly Precipitation Climatology from Gauge Observations and Satellite Estimates
206A (Henry B. Gonzalez Convention Center)
Mingyue Chen, RS Information Systems, Inc., Camp Springs, MD; and P. Xie, J. Janowiak, and V. Kousky
Poster PDF (287.9 kB)
Toward the construction of a global monthly precipitation climatology with improved representation of orographic effects, preliminary work has been conducted to compare two sets of precipitation climatologies, to explore the relationship between precipitation enhancements and local orography, and to develop a prototype algorithm to create monthly climatology over the global land with orographic consideration.

First, two gauge-based precipitation climatologies, the PRISM of Daly et al. (1994) and the PREC/L of Chen et al. (2002), are compared over a 0.5o lat/lon grid over the contiguous United States to examine how orographic enhancements in precipitation may impact the quantitative accuracy in the interpolated precipitation fields over mountainous areas. In the PRISM, the monthly precipitation climatology is calculated via a rainfall elevation relationship that has been established empirically for each calendar month by local comparisons, while in the PREC/L it is defined by an inverse-distance interpolation of gauge observations with no orographic considerations. While good agreement in both quantitative magnitude and spatial distribution patterns are observed between the two climatologies over areas with relatively flat terrain, significant underestimates are reported in the PREC/L compared to the PRISM over the mountainous areas in the western United States. Quantitative examinations of the two climatologies over the mountainous areas reveal a clear relationship between the departures in precipitation climatology and the grid box elevation, implying that the differences between the PREC/L and the PRISM are related to differences in the manner in which orographic effects are handled.

To understand how precipitation is influenced by topography, the relationship between the monthly station precipitation climatology and station elevation is examined statistically using the GHCN Version 2 data set. Orographic enhancements in precipitation are observed over all mountainous areas examined in this study. In a selected region, the monthly precipitation climatology at a station increases with its elevation at a rate that differs for different seasons and for different locations relative to the wind direction. This rate of orographic enhancement in precipitation, however, can be expressed very well as a linear function of the mean precipitation over the region regardless of season and relative location. The existence of this linear relationship is confirmed for all of the regions examined in this study, but the coefficients in the relationship differ regionally.

Based on these results, a prototype algorithm is being developed at NOAA Climate Prediction Center (CPC) to construct a monthly precipitation climatology over the global land areas with improved representation for orographic enhancements in precipitation by combined use of gauge and satellite observations. Gauge-observed monthly precipitation climatology is defined for over 30,000 stations using data collected from the Global Historical Climatology Network (GHCN) of NOAA/NCDC, Climate Anomaly Monitoring System (CAMS) of NOAA/CPC and several other data sets acquired through various CPC activities. Monthly precipitation climatology derived from the TRMM Precipitation Radar (PR) and SSM/I Passive Microwave (PM) observations are defined for an 8-year period from 1998 to 2005. The raw satellite-based precipitation climatology is then adjusted against the gauge-based climatology to remove large-scale bias. The gauge station climatology and the bias-corrected satellite data are finally combined through an optimal interpolation (OI) based technique, in which analyzed precipitation climatology is dominated by gauge observations over locations with dense gauge networks, while over gauge sparse areas its spatial distribution is controlled by satellite observations and its magnitude is influenced by nearby gauge data with topographic consideration. Details of the algorithm and the resulting precipitation climatology will be reported at the meeting.

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