Abstract
By using an ensemble data assimilation system, this chapter reviews the impact of assimilating observable and retrievable ground-based scanning weather radar information. Pseudo-observations of temperature, humidity, and dual-polarimetric parameters are assimilated in addition to radial velocity and reflectivity. Instead of assimilating the information inside the weather system, retrieved moisture information surrounding precipitation systems could be further obtained by collocated dual-wavelength radars. Via the studies of idealized and different high-impact weather cases, analyses and quantitative precipitation forecasts are examined and validated.
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References
Alpert JC, Kumar VK (2007) Radial wind super-obs from the WSR-88D radars in the NCEP operational assimilation system. Mon Weather Rev 135:1090–1109. https://doi.org/10.1175/MWR3324.1
Anderson JL (2001) An ensemble adjustment Kalman filter for data assimilation. Mon Weather Rev 129:2884–2903. https://doi.org/10.1175/1520-0493(2001)129%3c2884:AEAKFF%3e2.0.CO;2
Bishop CH, Etherton BJ, Majumdar SJ (2001) Adaptive sampling with the ensemble transform Kalman filter. Part I: theoretical aspects. Mon Weather Rev 129:420–436. https://doi.org/10.1175/1520-0493(2001)129%3c0420:ASWTET%3e2.0.CO;2
Carlin JT, Gao J, Snyder JC et al (2017) Assimilation of ZDR columns for improving the spinup and forecast of convective storms in storm-scale models: proof-of-concept experiments. Mon Weather Rev 145:5033–5057. https://doi.org/10.1175/MWR-D-17-0103.1
Caumont O, Cimini D, Löhnert U et al (2016) Assimilation of humidity and temperature observations retrieved from ground-based microwave radiometers into a convective-scale NWP model. Quart J Roy Meteor Soc 142:2692–2704. https://doi.org/10.1002/qj.2860
Do PN, Chung KS, Lin PL et al (2022) Assimilating retrieved water vapor and radar data from NCAR S-PolKa: performance and validation using real cases. Mon Weather Rev 150:1177–1199. https://doi.org/10.1175/MWR-D-21-0292.1
Ellis SM, Vivekanandan J (2010) Water vapor estimates using simultaneous dual-wavelength radar observations. Radio Sci 45, RS5002, https://doi.org/10.1029/2009RS004280
Evensen G (1994) Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J Geophys Res 99:10143–10162. https://doi.org/10.1029/94JC00572
Fabry F, Meunier V (2020) Why are radar data so difficult to assimilate skillfully? Mon Weather Rev 148:2819–2836. https://doi.org/10.1175/MWR-D-19-0374.1
Ge G, Gao J, Xue M (2013) Impacts of assimilating measurements of different state variables with a simulated supercell storm and three-dimensional variational method. Mon Weather Rev 141:2759–2777. https://doi.org/10.1175/MWR-D-12-00193.1
Hunt BR, Kostelich EJ, Szunyogh I (2007) Efficient data assimilation for spatiotemporal chaos: a local ensemble transform Kalman filter. Phys D 230:112–126. https://doi.org/10.1016/j.physd.2006.11.008
Jung Y, Zhang G, Xue M (2008) Assimilation of simulated polarimetric radar data for a convective storm using the ensemble Kalman filter. Part I: observation operators for reflectivity and polarimetric variables. Mon Weather Rev 136:2228–2245. https://doi.org/10.1175/2007MWR2083.1
Kang JS, Kalnay E, Liu J et al (2011) “Variable localization” in an ensemble Kalman filter: application to the carbon cycle data assimilation. J Geophys Res 116:D09110. https://doi.org/10.1029/2010JD014673
Ke CY, Chung KS, Liou YC, Tsai CC (2022) Impact of assimilating radar data with 3D thermodynamic fields in an ensemble Kalman filter: proof of concept and feasibility. Mon Weather Rev 150:3251–3273. https://doi.org/10.1175/MWR-D-22-0128.1
Kerr CA, Stensrud DJ, Wang X (2015) Assimilation of cloud-top temperature and radar observations of an idealized splitting supercell using an observing system simulation experiment. Mon Weather Rev 143:1018–1034. https://doi.org/10.1175/MWR-D-14-00146.1
Lai A, Gao J, Koch SE et al (2019) Assimilation of radar radial velocity, reflectivity and pseudo water vapor for convective-scale NWP in a variational framework. Mon Weather Rev 147:2877–2900. https://doi.org/10.1175/MWR-D-18-0403.1
Le Dimet F-X, Talagrand O (1986) Variational algorithms for analysis and assimilation of meteorological observations: theoretical aspects. Tellus A 38A:97–110. https://doi.org/10.1111/j.1600-0870.1986.tb00459.x
Li Y, Wang X, Xue M (2012) Assimilation of radar radial velocity data with the WRF hybrid ensemble-3DVAR system for the prediction of Hurricane Ike (2008). Mon Weather Rev 140:3507–3524. https://doi.org/10.1175/MWR-D-12-00043.1
Lindskog M, Salonen K, Järvinen H et al (2004) Doppler radar wind data assimilation with HIRLAM 3DVAR. Mon Weather Rev 132:1081–1092. https://doi.org/10.1175/1520-0493(2004)132%3c1081:DRWDAW%3e2.0.CO;2
Liou YC (2001) The derivation of absolute potential temperature perturbations and pressure gradients from wind measurements in three-dimensional space. J Atmos Ocean Technol 18:577–590. https://doi.org/10.1175/1520-0426(2001)018%3c0577:TDOAPT%3e2.0.CO;2
Liou YC, Chen Wang TC, Chung KS (2003) A three-dimensional variational approach for deriving the thermodynamic structure using Doppler wind observations—an application to a subtropical squall line. J Appl Meteorol 42:1443–1454. https://doi.org/10.1175/1520-0450(2003)042%3c1443:ATVAFD%3e2.0.CO;2
Liou YC, Chiou JL, Chen WH et al (2014) Improving the model convective storm quantitative precipitation nowcasting by assimilating state variables retrieved from multiple-Doppler radar observations. Mon Weather Rev 142:4017–4035. https://doi.org/10.1175/MWR-D-13-00315.1
Liou YC, Yang PC, Wang WY (2019) Thermodynamic recovery of the pressure and temperature fields over complex terrain using wind fields derived by multiple-Doppler radar synthesis. Mon Weather Rev 147:3843–3857. https://doi.org/10.1175/MWR-D-19-0059.1
Marshall JS, Palmer WMK (1948) The distribution of raindrops with size. J Meteorol 5:165–166. https://doi.org/10.1175/1520-0469(1948)005%3c0165:TDORWS%3e2.0.CO;2
Miyoshi T (2011) The Gaussian approach to adaptive covariance inflation and its implementation with the local ensemble transform Kalman filter. Mon Weather Rev 139:1519–1535. https://doi.org/10.1175/2010MWR3570.1
Ott E, Hunt BR, Szunyogh I et al (2004) A local ensemble Kalman filter for atmospheric data assimilation. Tellus A Dyn Meteorol Oceanogr 56:415–428. https://doi.org/10.3402/tellusa.v56i5.14462
Roux F (1985) Retrieval of thermodynamic fields from multiple-Doppler radar data using the equations of motion and the thermodynamic equation. Mon Weather Rev 113:2142–2157. https://doi.org/10.1175/1520-0493(1985)113%3c2142:ROTFFM%3e2.0.CO;2
Skamarock WC, Klemp JB, Dudhia J et al (2021) A description of the advanced research WRF model version 4.3. National Center for Atmospheric Research, Boulder, CO, p 145. https://doi.org/10.5065/1dfh-6p97
Sun J (2005) Initialization and numerical forecasting of a supercell storm observed during STEPS. Mon Weather Rev 133:793–813. https://doi.org/10.1175/MWR2887.1
Sun J, Zhang Y, Ban J et al (2020) Impact of combined assimilation of radar and rainfall data on short-term heavy rainfall prediction: a case study. Mon Weather Rev 148:2211–2232. https://doi.org/10.1175/MWR-D-19-0337.1
Tao W-K, Simpson J, Baker D et al (2003) Microphysics, radiation and surface processes in the Goddard Cumulus Ensemble (GCE) model. Meteorol Atmos Phys 82:97–137. https://doi.org/10.1007/s00703-001-0594-7
Tsai CC, Chung KS (2020) Sensitivities of quantitative precipitation forecasts for Typhoon Soudelor (2015) near landfall to polarimetric radar data assimilation. Remote Sens 12(22):3711. https://doi.org/10.3390/rs12223711
Tsai CC, Yang SC, Liou YC (2014) Improving quantitative precipitation nowcasting with a local ensemble transform Kalman filter radar data assimilation system: observing system simulation experiments. Tellus A Dyn Meteorol Oceanogr 66:21804. https://doi.org/10.3402/tellusa.v66.21804
Wattrelot E, Caumont O, Mahfouf JF (2014) Operational implementation of the 1D13D-Var assimilation method of radar reflectivity data in the AROME model. Mon Weather Rev 142:1852–1873. https://doi.org/10.1175/MWR-D-13-00230.1
Whitaker JS, Hamill TM (2002) Ensemble data assimilation without perturbed observations. Mon Weather Rev 130:1913–1924. https://doi.org/10.1175/1520-0493(2002)130%3c1913:EDAWPO%3e2.0.CO;2
Xiao Q, Sun J (2007) Multiple-radar data assimilation and short-range quantitative precipitation forecasting of a squall line observed during IHOP_2002. Mon Weather Rev 135:3381–3404. https://doi.org/10.1175/MWR3471.1
You CR, Chung KS, Tsai CC (2020) Evaluating the performance of a convection-permitting model by using dual-polarimetric radar parameters: case study of SoWMEX IOP8. Remote Sens 12(18):3004. https://doi.org/10.3390/rs12183004
Zhang F, Weng Y, Sippel JA et al (2009) Cloud-resolving hurricane initialization and prediction through assimilation of Doppler radar observations with an ensemble Kalman filter. Mon Weather Rev 137:2105–2125. https://doi.org/10.1175/2009MWR2645.1
Zrnic DS, Ryzhkov AV (1999) Polarimetry for weather surveillance radars. Bull Am Meteorol Soc 80:389–406. https://doi.org/10.1175/1520-0477(1999)080%3c0389:PFWSR%3e2.0.CO;2
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Chung, KS., Tsai, CC., Ke, CY., Do, PN., Liou, YC. (2023). Evaluating the Assimilation of Observable and Retrievable Weather Radar Information for Quantitative Precipitation Forecasts. In: Park, S.K. (eds) Numerical Weather Prediction: East Asian Perspectives. Springer Atmospheric Sciences. Springer, Cham. https://doi.org/10.1007/978-3-031-40567-9_9
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