Estimating the uncertainty of areal precipitation using data assimilation

C. Merker; G. Geppert; M. Clemens; F. Ament

doi:10.1080/16000870.2019.1606666

Estimating the uncertainty of areal precipitation using data assimilation

Tellus

Volume 71 (2019): Issue 1

By: C. Merker, G. Geppert, M. Clemens and F. Ament

Open Access

|Jan 2019

Alpert, J. C. and Kumar, V. K. 2007. Radial wind super-obs from the WSR-88D radars in the NCEP operational assimilation system. Mon. Weather Rev. 135, 1090–1109. doi: 10.1175/MWR3324.1
Open DOI Search in Google Scholar Back to article
Atencia, A. and Zawadzki, I. 2014. A comparison of two techniques for generating nowcasting ensembles. Part I: Lagrangian ensemble technique. Mon. Weather Rev. 142, 4036–4052. doi: 10.1175/MWR-D-13-00117.1
Open DOI Search in Google Scholar Back to article
Atencia, A. and Zawadzki, I. 2015. A comparison of two techniques for generating nowcasting ensembles. Part II: analogs selection and comparison of techniques. Mon. Weather Rev. 143, 2890–2908. doi: 10.1175/MWR-D-14-00342.1
Open DOI Search in Google Scholar Back to article
Berenguer, M. and Zawadzki, I. 2008. A study of the error covariance matrix of radar rainfall estimates in stratiform rain. Weather Forecast. 23, 1085–1101. doi: 10.1175/2008WAF2222134.1
Open DOI Search in Google Scholar Back to article
Berndt, C., Rabiei, E. and Haberlandt, U. 2014. Geostatistical merging of rain gauge and radar data for high temporal resolutions and various station density scenarios. J. Hydrol. 508, 88–101. doi: 10.1016/j.jhydrol.2013.10.028
Open DOI Search in Google Scholar Back to article
Bianchi, B., van Leeuwen, P. J., Hogan, R. J. and Berne, A. 2013. A variational approach to retrieve rain rate by combining information from rain gauges, radars, and microwave links. J. Hydrometeorl. 14, 1897–1909. doi: 10.1175/JHM-D-12-094.1
Open DOI Search in Google Scholar Back to article
Bick, T., Simmer, C., Trömel, S., Wapler, K., Hendricks Franssen, H.-J. and co-authors. 2016. Assimilation of 3D radar reflectivities with an ensemble Kalman filter on the convective scale. Q. J. R. Meteorol. Soc. 142, 1490–1504. doi: 10.1002/qj.2751
Open DOI Search in Google Scholar Back to article
Bishop, C. H., Etherton, B. J. and Majumdar, S. J. 2001. Adaptive sampling with the ensemble transform Kalman filter. Part I: theoretical aspects. Mon. Weather Rev. 129, 420–436. doi: 10.1175/1520-0493(2001)129<;0420:ASWTET>2.0.CO;2
Open DOI Search in Google Scholar Back to article
Bowler, N. E., Pierce, C. E. and Seed, A. W. 2006. STEPS: a probabilistic precipitation forecasting scheme which merges an extrapolation nowcast with downscaled NWP. Q. J. R. Meteorol. Soc. 132, 2127–2155. doi: 10.1256/qj.04.100
Open DOI Search in Google Scholar Back to article
Chang, W., Chung, K.-S., Fillion, L. and Baek, S.-J. 2014. Radar data assimilation in the Canadian high-resolution ensemble Kalman filter system: performance and verification with real summer cases. Mon. Weather Rev. 142, 2118–2138. doi: 10.1175/MWR-D-13-00291.1
Open DOI Search in Google Scholar Back to article
Chumchean, S., Seed, A. and Sharma, A. 2006. Correcting of real-time radar rainfall bias using a Kalman filtering approach. J. Hydrol. 317, 123–137. doi: 10.1016/j.jhydrol.2005.05.013
Open DOI Search in Google Scholar Back to article
Chung, K.-S., Zawadzki, I., Yau, M. K. and Fillion, L. 2009. Short-term forecasting of a midlatitude convective storm by the assimilation of single-doppler radar observations. Mon. Weather Rev. 137, 4115–4135. doi: 10.1175/2009MWR2731.1
Open DOI Search in Google Scholar Back to article
Ciach, G. J., Krajewski, W. F. and Villarini, G. 2007. Product-error-driven uncertainty model for probabilistic quantitative precipitation estimation with NEXRAD data. J. Hydrometeorl. 8, 1325–1347. doi: 10.1175/2007JHM814.1
Open DOI Search in Google Scholar Back to article
Creutin, J. D., Delrieu, G. and Lebel, T. 1988. Rain measurement by raingage-radar combination: a geostatistical approach. J. Atmos. Oceanic Technol. 5, 102–115. doi: 10.1175/1520-0426(1988)005<;0102:RMBRRC>2.0.CO;2
Open DOI Search in Google Scholar Back to article
Dai, Q., Han, D., Rico-Ramirez, M. and Srivastava, P. K. 2014. Multivariate distributed ensemble generator: a new scheme for ensemble radar precipitation estimation over temperate maritime climate. J. Hydrol. 511, 17–27. doi: 10.1016/j.jhydrol.2014.01.016
Open DOI Search in Google Scholar Back to article
Dai, Q., Rico-Ramirez, M. A., Han, D., Islam, T. and Liguori, S. 2015. Probabilistic radar rainfall nowcasts using empirical and theoretical uncertainty models. Hydrol. Process. 29, 66–79. doi: 10.1002/hyp.10133
Open DOI Search in Google Scholar Back to article
Dowell, D. C., Wicker, L. J. and Snyder, C. 2011. Ensemble Kalman filter assimilation of radar observations of the 8 May 2003 Oklahoma City Supercell: influences of reflectivity observations on storm-scale analyses. Mon. Weather Rev. 139, 272–294. doi: 10.1175/2010MWR3438.1
Open DOI Search in Google Scholar Back to article
Fielding, M. D., Chiu, J. C., Hogan, R. J. and Feingold, G. 2014. A novel ensemble method for retrieving properties of warm cloud in 3-D using ground-based scanning radar and zenith radiances. J. Geophys. Res. Atmos. 119, 10912–10930. doi: 10.1002/2014JD021742
Open DOI Search in Google Scholar Back to article
Germann, U., Berenguer, M., Sempere-Torres, D. and Zappa, M. 2009. REALEnsemble radar precipitation estimation for hydrology in a mountainous region. Q. J. R. Meteorol. Soc. 135, 445–456. doi: 10.1002/qj.375
Open DOI Search in Google Scholar Back to article
Grum, M., Kraemer, S., Verworn, H.-R. and Redder, A. 2005. Combined use of point rain gauges, radar, microwave link and level measurements in urban hydrological modelling. Atmos. Res. 77, 313–321. doi: 10.1016/j.atmosres.2004.10.013
Open DOI Search in Google Scholar Back to article
Haberlandt, U. 2007. Geostatistical interpolation of hourly precipitation from rain gauges and radar for a large-scale extreme rainfall event. J. Hydrol. 332, 144–157. doi: 10.1016/j.jhydrol.2006.06.028
Open DOI Search in Google Scholar Back to article
van Horne, MP. 2003. Short-Term Precipitation Nowcasting for Composite Radar Rain Fields. Master’s Thesis, MIT.
Search in Google Scholar Back to article
Hunt, B. R., Kostelich, E. J. and Szunyogh, I. 2007. Efficient data assimilation for spatiotemporal chaos: a local ensemble transform Kalman filter. Physica D 230, 112–126. doi: 10.1016/j.physd.2006.11.008
Open DOI Search in Google Scholar Back to article
Jacques, D. and Zawadzki, I. 2014. The impacts of representing the correlation of errors in radar data assimilation. Part I: experiments with simulated background and observation estimates. Mon. Weather Rev. 142, 3998–4016. doi: 10.1175/MWR-D-14-00104.1
Open DOI Search in Google Scholar Back to article
Jacques, D. and Zawadzki, I. 2015. The impacts of representing the correlation of errors in radar data assimilation. Part II: model output as background estimates. Mon. Weather Rev. 143, 2637–2656. doi: 10.1175/MWR-D-14-00243.1
Open DOI Search in Google Scholar Back to article
Kalman, R. E. 1960. A new approach to linear filtering and prediction problems. J. Basic Eng. 82, 35–45. doi: 10.1115/1.3662552
Open DOI Search in Google Scholar Back to article
Kalman, R. E. and Bucy, R. S. 1961. New results in linear filtering and prediction theory. J. Basic Eng. 83, 95–108. doi: 10.1115/1.3658902
Open DOI Search in Google Scholar Back to article
Krajewski, W. F. 1987. Cokriging radar-rainfall and rain gage data. J. Geophys. Res. 92, 9571–9580. doi: 10.1029/JD092iD08p09571
Open DOI Search in Google Scholar Back to article
Krajewski, W. F. and Ciach, G. J. Towards Probabilistic Quantitative Precipitation WSR-88D Algorithms: Data Analysis and Development of Ensemble Generator Model: Phase 4. Technical report, NOAA, 02 2006.
Search in Google Scholar Back to article
Lengfeld, K., Clemens, M., Münster, H. and Ament, F. 2014. Performance of high-resolution X-band weather radar networks—the PATTERN example. Atmos. Meas. Tech. 7, 4151–4166. doi: 10.5194/amt-7-4151-2014
Open DOI Search in Google Scholar Back to article
Leutbecher, M. and Palmer, T. N. 2008. Ensemble forecasting. J. Comput. Phys. 227, 3515–3539. doi: 10.1016/j.jcp.2007.02.014
Open DOI Search in Google Scholar Back to article
Livings, D. M., Dance, S. L. and Nichols, N. K. 2008. Unbiased ensemble square root filters. Physica D 237, 1021–1028. doi: 10.1016/j.physd.2008.01.005
Open DOI Search in Google Scholar Back to article
Mercier, F., Barthès, L. and Mallet, C. 2015. Estimation of finescale rainfall fields using broadcast TV satellite links and a 4DVAR assimilation method. J. Atmos. Oceanic Technol. 32, 1709–1728. doi: 10.1175/JTECH-D-14-00125.1
Open DOI Search in Google Scholar Back to article
Merker, C. 2017. Estimating the Uncertainty of Areal Precipitation using Data Assimilation. PhD thesis, Universität Hamburg.
Search in Google Scholar Back to article
Ott, E., Hunt, B. R., Szunyogh, I., Zimin, A. V., Kostelich, E. J. and co-authors. 2004. A local ensemble Kalman filter for atmospheric data assimilation. Tellus A 56, 415–428. doi: 10.1111/j.1600-0870.2004.00076.x
Open DOI Search in Google Scholar Back to article
Park, T., Lee, T., Ahn, S. and Lee, D. 2016. Error influence of radar rainfall estimate on rainfall-runoff simulation. Stoch. Environ. Res. Risk Assess. 30, 283–292. doi: 10.1007/s00477-015-1090-9
Open DOI Search in Google Scholar Back to article
Pulkkinen, S., Koistinen, J., Kuitunen, T. and Harri, A.-M. 2016. Probabilistic radar-gauge merging by multivariate spatiotemporal techniques. J. Hydrol. 542, 662–678. doi: 10.1016/j.jhydrol.2016.09.036
Open DOI Search in Google Scholar Back to article
Rico-Ramirez, M. A., Liguori, S. and Schellart, A. N. A. 2015. Quantifying radar-rainfall uncertainties in urban drainage flow modelling. J. Hydrol. 528, 17–28. doi: 10.1016/j.jhydrol.2015.05.057
Open DOI Search in Google Scholar Back to article
Sideris, I. V., Gabella, M., Erdin, R. and Germann, U. 2014. Real-time radarrain-gauge merging using spatio-temporal co-kriging with external drift in the alpine terrain of Switzerland. Q. J. R. Meteorol. Soc. 140, 1097–1111. doi: 10.1002/qj.2188
Open DOI Search in Google Scholar Back to article
Simonin, D., Ballard, S. P. and Li, Z. 2014. Doppler radar radial wind assimilation using an hourly cycling 3D-Var with a 1.5 km resolution version of the Met Office Unified Model for nowcasting. Q. J. R. Meteorol. Soc. 140, 2298–2314. doi: 10.1002/qj.2298
Open DOI Search in Google Scholar Back to article
Smith, J. A. and Krajewski, W. F. 1991. Estimation of the mean field bias of radar rainfall estimates. J. Appl. Meteorol. 30, 397–412. doi: 10.1175/1520-0450(1991)030<;0397:EOTMFB>2.0.CO;2
Open DOI Search in Google Scholar Back to article
Talagrand, O. 1997. Assimilation of observations, an introduction. J. Meteorol. Soc. Jpn 75, 191–209. doi: 10.2151/jmsj1965.75.1B_191
Open DOI Search in Google Scholar Back to article
Tong, M. and Xue, M. 2005. Ensemble Kalman filter assimilation of Doppler Radar data with a compressible nonhydrostatic model: OSS experiments. Mon. Weather Rev. 133, 1789–1807. doi: 10.1175/MWR2898.1
Open DOI Search in Google Scholar Back to article
Villarini, G. and Krajewski, W. F. 2009. Empirically based modelling of radar-rainfall uncertainties for a C-band radar at different time-scales. Q. J. R. Meteorol. Soc. 135, 1424–1438. doi: 10.1002/qj.454
Open DOI Search in Google Scholar Back to article
Villarini, G. and Krajewski, W. F. 2010. Review of the different sources of uncertainty in single polarization radar-based estimates of rainfall. Surv. Geophys. 31, 107–129. doi: 10.1007/s10712-009-9079-x
Open DOI Search in Google Scholar Back to article
Waller, J. A., Dance, S. L. and Nichols, N. K. 2017. On diagnosing observation error statistics with local ensemble data assimilation. Q. J. R. Meteorol. Soc. 143, 2677–2686. doi: 10.1002/qj.3117
Open DOI Search in Google Scholar Back to article
Waller, J. A., Simonin, D., Dance, S. L., Nichols, N. K. and Ballard, S. P. 2016. Diagnosing observation error correlations for Doppler radar radial winds in the met office UKV model using observation-minus-background and observation-minus-analysis statistics. Mon. Weather Rev. 144, 3533–3551. doi: 10.1175/MWR-D-15-0340.1
Open DOI Search in Google Scholar Back to article
Xu, Q., Nai, K. and Wei, L. 2007. An innovation method for estimating radar radial-velocity observation error and background wind error covariances. Q. J. R. Meteorol. Soc. 133, 407–415. doi: 10.1002/qj.21
Open DOI Search in Google Scholar Back to article
Zinevich, A., Messer, H. and Alpert, P. 2009. Frontal rainfall observation by a commercial microwave communication network. J. Appl. Meteorol. Climatol. 48, 1317–1334. doi: 10.1175/2008JAMC2014.1
Open DOI Search in Google Scholar Back to article

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DOI: https://doi.org/10.1080/16000870.2019.1606666 | Journal eISSN: 3035-9554

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Language: English

Page range: 1606666 - 1606666

Published on: Jan 1, 2019

Published by: Stockholm University Press

In partnership with: Paradigm Publishing Services

Keywords:

data assimilation,

precipitation measurements,

uncertainty estimate,

rain radar,

nowcasting

© 2019 C. Merker, G. Geppert, M. Clemens, F. Ament, published by Stockholm University Press
This work is licensed under the Creative Commons Attribution 4.0 License.

Volume 71 (2019): Issue 1