In this article, we present an assimilation impact study for forecasting hurricane Sandy using a three‐dimensional variational data assimilation system (3DVAR). In particular, we employ the 3DVAR component of the Weather Research and Forecasting Model and conduct analysis/forecast cycling experiments for “control” and “radiance” assimilation cases for the hurricane Sandy period. In “control” assimilation experiment, only conventional air and surface observations data are assimilated, while, in “radiance” assimilation experiment, along with the conventional air and surface observations data, the satellite radiance data from the Advanced Microwave Sounding Unit-A (AMSU-A) and the Microwave Humidity Sounder (MHS) sensors are also assimilated. For the radiance assimilation, we employ the community radiative transfer model as the forward operator and perform quality control and bias correction procedure before the radiance data are assimilated. In order to assess the impact of the assimilation experiments, we produce 132-h deterministic forecast starting on 00 UTC October 25, 2012. The results reveal that, in particular, the assimilation of AMSU-A satellite radiances helps to improve the short- to medium-range forecast (up to ~60-h lead time). The forecast skill is degraded in the long-range forecast (beyond 60 h) with the AMSU-A assimilation.
Variational data assimilation Numerical weather prediction (NWP) Cyclone forecast Track propagation WRF 3DVAR Radiative transfer ATOVS AMSU-A AMSU-B MHS
This is a preview of subscription content, log in to check access.
Part of this research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (NASA). The data for this study are from NOAA’s National Operational Model Archive and Distribution System (NOMADS) which is maintained at NOAA’s National Climatic Data Center (NCDC). The views expressed here are those of the authors solely and do not constitute a statement of policy, decision, or position on behalf of NOAA, NASA, or the authors’ affiliated institutions.
Chambon P, Zhang SQ, Hou AY, Zupanski M, Cheung S (2014) Assessing the impact of pre-GPM microwave precipitation observations in the Goddard WRF ensemble data assimilation system. Q J R Meteorol Soc 140(681):1219–1235. doi:10.1002/qj.2215CrossRefGoogle Scholar
Hong SY, Noh Y, Dudhia J (2006) A new vertical diffusion package with an explicit treatment of entrainment processes. Mon Weather Rev 134(9):2318–2341. doi:10.1175/mwr3199.1CrossRefGoogle Scholar
Ishak A, Remesan R, Srivastava P, Islam T, Han DW (2013) Error correction modelling of wind speed through hydro-meteorological parameters and mesoscale model: a hybrid approach. Water Resour Manag 27(1):1–23. doi:10.1007/s11269-012-0130-1CrossRefGoogle Scholar
Islam T, Rico-Ramirez MA, Han DW, Bray M, Srivastava PK (2013) Fuzzy logic based melting layer recognition from 3 GHz dual polarization radar: appraisal with NWP model and radio sounding observations. Theor Appl Climatol 112(1–2):317–338. doi:10.1007/s00704-012-0721-zCrossRefGoogle Scholar
Islam T, Rico-Ramirez MA, Han DW, Srivastava PK (2014) Sensitivity associated with bright band/melting layer location on radar reflectivity correction for attenuation at C-band using differential propagation phase measurements. Atmos Res 135:143–158. doi:10.1016/j.atmosres.2013.09.003CrossRefGoogle Scholar
Islam T, Srivastava PK, Rico-Ramirez MA, Dai Q, Gupta M, Singh SK (2015) Tracking a tropical cyclone through WRF–ARW simulation and sensitivity of model physics. Nat Hazards 76(3):1473–1495. doi:10.1007/s11069-014-1494-8CrossRefGoogle Scholar
Jones TA, Stensrud DJ (2012) Assimilating AIRS temperature and mixing ratio profiles using an ensemble Kalman filter approach for convective-scale forecasts. Weather Forecast 27(3):541–564. doi:10.1175/waf-d-11-00090.1CrossRefGoogle Scholar
Liu ZQ, Schwartz CS, Snyder C, Ha SY (2012) Impact of assimilating AMSU-A radiances on forecasts of 2008 Atlantic tropical cyclones initialized with a limited-area ensemble Kalman filter. Mon Weather Rev 140(12):4017–4034. doi:10.1175/mwr-d-12-00083.1CrossRefGoogle Scholar
Singh R, Kishtawal CM, Pal PK, Joshi PC (2012) Improved tropical cyclone forecasts over north Indian Ocean with direct assimilation of AMSU-A radiances. Meteorol Atmos Phys 115(1–2):15–34. doi:10.1007/s00703-011-0165-5CrossRefGoogle Scholar
Srivastava PK, Han DW, Ramirez MAR, Islam T (2013) Comparative assessment of evapotranspiration derived from NCEP and ECMWF global datasets through Weather Research and Forecasting model. Atmos Sci Lett 14(2):118–125. doi:10.1002/asl2.427CrossRefGoogle Scholar
Subramani D, Chandrasekar R, Ramanujam KS, Balaji C (2014) A new ensemble-based data assimilation algorithm to improve track prediction of tropical cyclones. Nat Hazards 71(1):659–682. doi:10.1007/s11069-013-0942-1CrossRefGoogle Scholar
Zhang SQ, Zupanski M, Hou AY, Lin X, Cheung SH (2013) Assimilation of precipitation-affected radiances in a cloud-resolving WRF ensemble data assimilation system. Mon Weather Rev 141(2):754–772. doi:10.1175/mwr-d-12-00055.1CrossRefGoogle Scholar
Zou XL, Qin ZK, Weng FZ (2013) Improved quantitative precipitation forecasts by MHS radiance data assimilation with a newly added cloud detection algorithm. Mon Weather Rev 141(9):3203–3221. doi:10.1175/mwr-d-13-00009.1CrossRefGoogle Scholar
Zupanski D, Zhang SQ, Zupanski M, Hou AY, Cheung SH (2011) A prototype WRF-based ensemble data assimilation system for dynamically downscaling satellite precipitation observations. J Hydrometeorol 12(1):118–134. doi:10.1175/2010jhm1271.1CrossRefGoogle Scholar