Website

http://jetzlab.yale.edu/

Description

Conservation of biodiversity demands comprehension of evolutionary and ecological patterns and processes that occur over vast spatial and temporal scales. A central goal of ecology is to understand the factors that control the spatial distribution of species and this has become even more important in the face of climate change. However, at global scales there can be enormous uncertainty in environmental data used to model species distributions. Even ‘simple’ metrics such as mean annual precipitation are difficult to estimate in areas with few weather stations and available data sets do not quantify uncertainty in these surfaces. We are developing a global, 1km resolution, daily meteorological dataset for 1970-2010 by leveraging relatively high quality station observations with spatially continuous but indirect satellite observations. While several satellite derived precipitation products exist, all are relatively coarse (≥0.25 to estimate other cloud parameters that are more directly related to precipitation. Precipitation will fall when cloud particles achieve sufficient mass to overcome updraft winds and when the clouds have sufficient vertical extent to facilitate the growth of these particles. The MODIS Cloud Product (MOD06) includes estimates of effective radius and optical thickness at 1km resolution. These parameters are theoretically related to precipitation and we are exploring their utility in the interpolation of station precipitation observations. Improving high resolution estimates of precipitation will facilitate analysis of geographic and/or environmental shifts in species distributions in response to global climate change.). Incorporating satellite-derived data into station interpolation has enormous potential to improve the quality of high resolution global climate layers. Through climate-aided interpolation, these climate layers can be used to estimate variability across finer temporal scales (monthly or daily). We have completed a regional assessment of the value of incorporating satellite-derived cloud frequency into the interpolation of precipitation and found that it significantly improves predictive accuracy.

 

Incorporating satellite derived cloud climatologies to improve high resolution interpolation of daily precipitation.

Conservation of biodiversity demands comprehension of evolutionary and ecological patterns and processes that occur over vast spatial and temporal scales. A central goal of ecology is to understand the factors that control the spatial distribution of species and this has become even more important in the face of climate change. However, at global scales there can be enormous uncertainty in environmental data used to model species distributions. Even ‘simple’ metrics such as mean annual precipitation are difficult to estimate in areas with few weather stations and available data sets do not quantify uncertainty in these surfaces. We are developing a global, 1km resolution, daily meteorological dataset for 1970-2010 by leveraging relatively high quality station observations with spatially continuous but indirect satellite observations. While several satellite derived precipitation products exist, all are relatively coarse (≥0.25 to estimate other cloud parameters that are more directly related to precipitation. Precipitation will fall when cloud particles achieve sufficient mass to overcome updraft winds and when the clouds have sufficient vertical extent to facilitate the growth of these particles. The MODIS Cloud Product (MOD06) includes estimates of effective radius and optical thickness at 1km resolution. These parameters are theoretically related to precipitation and we are exploring their utility in the interpolation of station precipitation observations. Improving high resolution estimates of precipitation will facilitate analysis of geographic and/or environmental shifts in species distributions in response to global climate change.). Incorporating satellite-derived data into station interpolation has enormous potential to improve the quality of high resolution global climate layers. Through climate-aided interpolation, these climate layers can be used to estimate variability across finer temporal scales (monthly or daily). We have completed a regional assessment of the value of incorporating satellite-derived cloud frequency into the interpolation of precipitation and found that it significantly improves predictive accuracy.

http://elischolar.library.yale.edu/dayofdata/2013/Posters/7