Next Article in Journal
Changes in Fungal Community Structure in Freshwater Canals across a Gradient of Urbanization
Next Article in Special Issue
Evaluation of an Application of Probabilistic Quantitative Precipitation Forecasts for Flood Forecasting
Previous Article in Journal
Effects of Travel Speed and Collector on Evaluation of the Water Application Uniformity of a Center Pivot Irrigation System
Previous Article in Special Issue
The Impacts of Soil Moisture Initialization on the Forecasts of Weather Research and Forecasting Model: A Case Study in Xinjiang, China
Open AccessArticle

Effect of Logarithmically Transformed IMERG Precipitation Observations in WRF 4D-Var Data Assimilation System

1
School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
2
NOAA/OAR/Global Systems Laboratory, Boulder, CO 80305, USA
3
Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO 80523, USA
*
Author to whom correspondence should be addressed.
Water 2020, 12(7), 1918; https://doi.org/10.3390/w12071918
Received: 21 May 2020 / Revised: 17 June 2020 / Accepted: 28 June 2020 / Published: 5 July 2020
Precipitation estimates from numerical weather prediction (NWP) models are uncertain. The uncertainties can be reduced by integrating precipitation observations into NWP models. This study assimilates Version 04 Integrated Multi-satellite Retrievals for the Global Precipitation Measurement (GPM) (IMERG) Final Run into the Weather Research and Forecasting (WRF) model data assimilation (WRFDA) system using a four-dimensional variational (4D-Var) method. Three synoptic-scale convective precipitation events over the central United States during 2015–2017 are used as case studies. To investigate the effect of logarithmically transformed IMERG precipitation in the WRFDA system, this study reports on several experiments with six-hour and hourly assimilation windows, regular (nontransformed) and logarithmically transformed observations, and a constant observation error in regular and logarithmic spaces. Results show that hourly assimilation windows improve precipitation simulations significantly compared to six-hour windows. Logarithmically transformed precipitation does not improve precipitation estimations relative to nontransformed precipitation. However, better predictions of heavy precipitation can be achieved with a constant error in the logarithmic space (corresponding to a linearly increasing error in the regular space), which modifies the threshold of rejecting observations, and thus utilizes more observations. This study provides a cost function with logarithmically transformed observations for the 4D-Var method in the WRFDA system for future investigations. View Full-Text
Keywords: GPM; IMERG; WRF; data assimilation; precipitation; satellite retrievals GPM; IMERG; WRF; data assimilation; precipitation; satellite retrievals
Show Figures

Figure 1

MDPI and ACS Style

Zhang, J.; Lin, L.-F.; Bras, R.L. Effect of Logarithmically Transformed IMERG Precipitation Observations in WRF 4D-Var Data Assimilation System. Water 2020, 12, 1918. https://doi.org/10.3390/w12071918

AMA Style

Zhang J, Lin L-F, Bras RL. Effect of Logarithmically Transformed IMERG Precipitation Observations in WRF 4D-Var Data Assimilation System. Water. 2020; 12(7):1918. https://doi.org/10.3390/w12071918

Chicago/Turabian Style

Zhang, Jiaying; Lin, Liao-Fan; Bras, Rafael L. 2020. "Effect of Logarithmically Transformed IMERG Precipitation Observations in WRF 4D-Var Data Assimilation System" Water 12, no. 7: 1918. https://doi.org/10.3390/w12071918

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop