TY - JOUR
T1 - Assessment of SCaMPR and NEXRAD Q2 precipitation estimates using Oklahoma Mesonet observations
AU - Stenz, Ronald
AU - Dong, Xiquan
AU - Xi, Baike
AU - Kuligowski, Robert J.
N1 - Publisher Copyright:
© 2014 American Meteorological Society.
PY - 2014
Y1 - 2014
N2 - Although satellite precipitation estimates provide valuable information for weather and flood forecasts, infrared (IR) brightness temperature (BT)-based algorithms often produce large errors for precipitation detection and estimation during deep convective systems (DCSs).AsDCSs produce greatly varying precipitation rates below similar IR BT retrievals, using IR BTs alone to estimate precipitation in DCSs is problematic. Classifying a DCS into convective-core (CC), stratiform (SR), and anvil cloud (AC) regions allows an evaluation of estimated precipitation distributions among DCS components to supplement typical quantitative precipitation estimate (QPE) evaluations and to diagnose these IR-based algorithmbiases. This paper assesses the performance of the National Mosaic and Multi-Sensor Next Generation Quantitative Precipitation Estimation System (NMQ Q2), and a simplified version of the Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR) algorithm, over the state of Oklahoma using Oklahoma Mesonet observations. While average annual Q2 precipitation estimates were about 35% higher than Mesonet observations, strong correlations exist between these two datasets for multiple temporal and spatial scales. Additionally, the Q2-estimated precipitation distribution among DCS components strongly resembled the Mesonet-observed distribution, indicatingQ2 can accurately capture the precipitation characteristics of DCSs despite its wet bias. SCaMPR retrievals were typically 3-4 times higher than Mesonet observations, with relatively weak correlations during 2012. Overestimates from SCaMPR retrievals were primarily caused by precipitation retrievals from the anvil regions of DCSs when collocated Mesonet stations recorded no precipitation. A modified SCaMPR retrieval algorithm, employing both cloud optical depth and IR temperature, has the potential to make significant improvements to reduce the wet bias of SCaMPR retrievals over anvil regions of a DCS.
AB - Although satellite precipitation estimates provide valuable information for weather and flood forecasts, infrared (IR) brightness temperature (BT)-based algorithms often produce large errors for precipitation detection and estimation during deep convective systems (DCSs).AsDCSs produce greatly varying precipitation rates below similar IR BT retrievals, using IR BTs alone to estimate precipitation in DCSs is problematic. Classifying a DCS into convective-core (CC), stratiform (SR), and anvil cloud (AC) regions allows an evaluation of estimated precipitation distributions among DCS components to supplement typical quantitative precipitation estimate (QPE) evaluations and to diagnose these IR-based algorithmbiases. This paper assesses the performance of the National Mosaic and Multi-Sensor Next Generation Quantitative Precipitation Estimation System (NMQ Q2), and a simplified version of the Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR) algorithm, over the state of Oklahoma using Oklahoma Mesonet observations. While average annual Q2 precipitation estimates were about 35% higher than Mesonet observations, strong correlations exist between these two datasets for multiple temporal and spatial scales. Additionally, the Q2-estimated precipitation distribution among DCS components strongly resembled the Mesonet-observed distribution, indicatingQ2 can accurately capture the precipitation characteristics of DCSs despite its wet bias. SCaMPR retrievals were typically 3-4 times higher than Mesonet observations, with relatively weak correlations during 2012. Overestimates from SCaMPR retrievals were primarily caused by precipitation retrievals from the anvil regions of DCSs when collocated Mesonet stations recorded no precipitation. A modified SCaMPR retrieval algorithm, employing both cloud optical depth and IR temperature, has the potential to make significant improvements to reduce the wet bias of SCaMPR retrievals over anvil regions of a DCS.
KW - Algorithms
KW - Microwave observations
KW - Radars/Radar observations
KW - Remote sensing
KW - Satellite observations
KW - Surface observations
UR - http://www.scopus.com/inward/record.url?scp=84915745661&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84915745661&partnerID=8YFLogxK
U2 - 10.1175/JHM-D-13-0199.1
DO - 10.1175/JHM-D-13-0199.1
M3 - Article
AN - SCOPUS:84915745661
SN - 1525-755X
VL - 15
SP - 2484
EP - 2500
JO - Journal of Hydrometeorology
JF - Journal of Hydrometeorology
IS - 6
ER -