ALBERT

Description: Validation of precipitation estimates from various products is a challenging problem, since the true precipitation is unknown. However, with the increased availability of precipitation estimates from a wide range of instruments (satellite, ground-based radar, and gauge), it is now possible to apply the triple collocation (TC) technique to characterize the uncertainties in each of the products. Classical TC takes advantage of three collocated data products of the same variable and estimates the mean squared error of each, without requiring knowledge of the truth. In this study, triplets among NEXRAD-IV, TRMM 3B42RT, GPCP 1DD, and GPI products are used to quantify the associated spatial error characteristics across a central part of the continental US. Data are aggregated to biweekly accumulations from January 2002 through April 2014 across a 2° × 2° spatial grid. This is the first study of its kind to explore precipitation estimation errors using TC across the US. A multiplicative (logarithmic) error model is incorporated in the original TC formulation to relate the precipitation estimates to the unknown truth. For precipitation application, this is more realistic than the additive error model used in the original TC derivations, which is generally appropriate for existing applications such as in the case of wind vector components and soil moisture comparisons. This study provides error estimates of the precipitation products that can be incorporated into hydrological and meteorological models, especially those used in data assimilation. Physical interpretations of the error fields (related to topography, climate, etc.) are explored. The methodology presented in this study could be used to quantify the uncertainties associated with precipitation estimates from each of the constellations of GPM satellites. Such quantification is prerequisite to optimally merging these estimates.

Description: Validation of precipitation estimates from various products is a challenging problem, since the true precipitation is unknown. However, with the increased availability of precipitation estimates from a wide range of instruments (satellite, ground-based radar, and gauge), it is now possible to apply the Triple Collocation (TC) technique to characterize the uncertainties in each of the products. Classical TC takes advantage of three collocated data products of the same variable and estimates the mean squared error of each, without requiring knowledge of the truth. In this study, triplets among NEXRAD-IV, TRMM 3B42, GPCP and GPI products are used to quantify the associated spatial error characteristics across a central part of the continental US. This is the first study of its kind to explore precipitation estimation errors using TC across the United States (US). A multiplicative (logarithmic) error model is incorporated in the original TC formulation to relate the precipitation estimates to the unknown truth. For precipitation application, this is more realistic than the additive error model used in the original TC derivations, which is generally appropriate for existing applications such as in the case of wind vector components and soil moisture comparisons. This study provides error estimates of the precipitation products that can be incorporated into hydrological and meteorological models, especially those used in data assimilation. Physical interpretations of the error fields (related to topography, climate, etc) are explored. The methodology presented in this study could be used to quantify the uncertainties associated with precipitation estimates from each of the constellation of GPM satellites. Such quantification is prerequisite to optimally merging these estimates.

Description: The terrestrial water and carbon cycles are coupled through plant regulation of stomatal closure. Both soil moisture and vapour pressure deficit - the amount of moisture in the air relative to its potential maximum - can govern stomatal closure, which reduces plant carbon uptake. However, plants vary in the degree to which they regulate their stomata - and in association, xylem conductance - in response to increasing aridity: isohydric plants exert tight regulation of stomata and the water content of the plant, whereas anisohydric plants do not. Here we use remote-sensing data sets of anisohydricity and vegetation greenness to show that productivity in United States grasslands - especially anisohydric ones - is far more sensitive to variations in vapour pressure deficit than to variations in precipitation. Anisohydric ecosystem productivity is over three times more sensitive to vapour pressure deficit than isohydric ecosystem productivity. The precipitation sensitivity of summer productivity increases with anisohydricity only for the most anisohydric ecosystems. We conclude that increases in vapour pressure deficit rather than changes in precipitation - both of which are expected impacts of climate change - will be a dominant influence on future grassland productivity. © 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.