Articles | Volume 14, issue 8
https://doi.org/10.5194/amt-14-5369-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-14-5369-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Physical characteristics of frozen hydrometeors inferred with parameter estimation
ECMWF, Shinfield Park, Reading, RG2 9AX, UK
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Cited articles
Aires, F., Prigent, C., Bernardo, F., Jiménez, C., Saunders, R., and Brunel,
P.: A Tool to Estimate Land-Surface Emissivities at Microwave frequencies
(TELSEM) for use in numerical weather prediction, Q. J. Roy. Meteorol.
Soc., 137, 690–699, https://doi.org/10.1002/qj.803, 2011. a
Allen, J. T., Tippett, M. K., Kaheil, Y., Sobel, A. H., Lepore, C., Nong, S.,
and Muehlbauer, A.: An extreme value model for US hail size, Mon. Weath.
Rev., 145, 4501–4519,
https://doi.org/10.1175/MWR-D-17-0119.1, 2017. a
Auligné, T., McNally, A. P., and Dee, D. P.: Adaptive bias correction for
satellite data in a numerical weather prediction system, Q. J. Roy.
Meteorol. Soc., 133, 631–642, https://doi.org/10.1002/qj.56,
2007. a
Bailey, M. P. and Hallett, J.: A comprehensive habit diagram for atmospheric
ice crystals: Confirmation from the laboratory, AIRS II, and other field
studies, J. Atmos. Sci., 66, 2888–2899,
https://doi.org/10.1175/2009JAS2883.1, 2009. a
Baordo, F. and Geer, A. J.: Assimilation of SSMIS humidity-sounding channels in all-sky conditions over land using a dynamic emissivity retrieval, Q. J. Roy. Meteorol. Soc., 142, 2854–2866, https://doi.org/10.1002/qj.2873, 2016. a
Barlakas, V. and Eriksson, P.: Three Dimensional Radiative Effects in Passive
Millimeter/Sub-Millimeter All-sky Observations, Remote Sens., 12, 531,
https://doi.org/10.3390/rs12030531, 2020. a, b
Barlakas, V., Geer, A. J., and Eriksson, P.: Introducing hydrometeor orientation into all-sky microwave and submillimeter assimilation, Atmos. Meas. Tech., 14, 3427–3447, https://doi.org/10.5194/amt-14-3427-2021, 2021. a, b
Bauer, P., Moreau, E., Chevallier, F., and O'Keeffe, U.: Multiple-scattering
microwave radiative transfer for data assimilation applications, Q. J.
Roy. Meteorol. Soc., 132, 1259–1281,
https://doi.org/10.1256/qj.05.153, 2006. a, b, c
Bechtold, P., Semane, N., Lopez, P., Chaboureau, J.-P., Beljaars, A., and
Bormann, N.: Representing equilibrium and nonequilibrium convection in
large-scale models, J. Atmos. Sci., 71, 734–753,
https://doi.org/10.1175/JAS-D-13-0163.1, 2014. a, b, c
Bell, W., Candy, B., Atkinson, N., Hilton, F., Baker, N., Bormann, N., Kelly,
G., Kazumori, M., Campbell, W., and Swadley, S.: The Assimilation of SSMIS
Radiances in Numerical Weather Prediction Models, IEEE Trans. Geosci. Remote
Sensing, 46, 884–900, https://doi.org/10.1109/TGRS.2008.917335, 2008. a
Bennartz, R. and Greenwald, T.: Current problems in scattering radiative
transfer modelling for data assimilation, Q. J. Roy. Meteorol. Soc., 137,
1952–1962, https://doi.org/10.1002/qj.953, 2011. a
Bocquet, M., Brajard, J., Carrassi, A., and Bertino, L.: Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and
expectation-maximization, Foundations of Data Science, 2, 55–80,
https://doi.org/10.3934/fods.2020004, 2020. a
Buehler, S. A., Jimenez, C., Evans, K., Eriksson, P., Rydberg, B., Heymsfield, A., Stubenrauch, C., Lohmann, U., Emde, C., John, V., Sreerekha, T. R., and Davis, C. P.: A concept for a satellite mission to measure cloud ice water path, ice particle size, and cloud altitude, Q. J. Roy. Meteorol. Soc., 133, 109–128,
https://doi.org/10.1002/qj.143, 2007. a
Courtier, P., Thépaut, J.-N., and Hollingsworth, A.: A strategy for
operational implementation of 4D-Var, using an incremental approach, Q.
J. Roy. Meteorol. Soc., 120, 1367–1387,
https://doi.org/10.1002/qj.49712051912, 1994. a, b
Dee, D.: Variational bias correction of radiance data in the ECMWF system,
in: ECMWF workshop proceedings: Assimilation of high spectral resolution
sounders in NWP, 28 June–1 July, 2004, Eur. Cent. for Med.
Range Weather Forecasts, Reading, UK, 97–112, available at: http://www.ecmwf.int (last access: 4 August 2021),
2004. a
Dee, D. P. and Da Silva, A. M.: Maximum-likelihood estimation of forecast and
observation error covariance parameters. Part I: Methodology, Mon. Weath.
Rev., 127, 1822–1834, https://doi.org/10.1175/1520-0493(1999)127<1822:MLEOFA>2.0.CO;2,
1999. a, b
Di Michele, S., Ahlgrimm, M., Forbes, R., Kulie, M., Bennartz, R., Janiskova,
M., and Bauer, P.: Interpreting an evaluation of the ECMWF global model
with CloudSat observations: ambiguities due to radar reflectivity forward
operator uncertainties, Q. J. Roy. Meteorol. Soc., 138, 2047–2065,
https://doi.org/10.1002/qj.1936, 2012. a
Doherty, A. M., Sreerekha, T. R., O'Keeffe, U. M., and English, S. J.: Ice
hydrometeor microphysical assumptions in radiative transfer models at
AMSU-B frequencies, Q. J. Roy. Meteorol. Soc., 133, 1205–1212,
https://doi.org/10.1002/qj.84, 2007. a
Draper, D. W., Newell, D. A., Wentz, F. J., Krimchansky, S., and
Skofronick-Jackson, G. M.: The global precipitation measurement (GPM)
microwave imager (GMI): Instrument overview and early on-orbit
performance, IEEE J. Sel. Top. App. Earth Obs. Rem. Sens., 8, 3452–3462,
https://doi.org/10.1109/JSTARS.2015.2403303, 2015. a
Duan, Q., Di, Z., Quan, J., Wang, C., Gong, W., Gan, Y., Ye, A., Miao, C.,
Miao, S., Liang, X., and Fan, S.: Automatic model calibration: a new way to
improve numerical weather forecasting, Bull. Amer. Meteorol. Soc., 98,
959–970, https://doi.org/10.1175/BAMS-D-15-00104.1, 2017. a, b
Duruisseau, F., Chambon, P., Wattrelot, E., Barreyat, M., and Mahfouf, J.-F.:
Assimilating cloudy and rainy microwave observations from SAPHIR on board
Megha Tropiques within the ARPEGE global model, Q. J. Roy.
Meteorol. Soc., 145, 620–641,
https://doi.org/10.1002/qj.3456, 2019. a
Ekelund, R., Eriksson, P., and Pfreundschuh, S.: Using passive and active observations at microwave and sub-millimetre wavelengths to constrain ice particle models, Atmos. Meas. Tech., 13, 501–520, https://doi.org/10.5194/amt-13-501-2020, 2020. a, b, c
Eriksson, P., Jamali, M., Mendrok, J., and Buehler, S. A.: On the microwave optical properties of randomly oriented ice hydrometeors, Atmos. Meas. Tech., 8, 1913–1933, https://doi.org/10.5194/amt-8-1913-2015, 2015. a, b, c
Eriksson, P., Ekelund, R., Mendrok, J., Brath, M., Lemke, O., and Buehler, S. A.: A general database of hydrometeor single scattering properties at microwave and sub-millimetre wavelengths, Earth Syst. Sci. Data, 10, 1301–1326, https://doi.org/10.5194/essd-10-1301-2018, 2018. a, b, c
Eriksson, P., Rydberg, B., Mattioli, V., Thoss, A., Accadia, C., Klein, U., and Buehler, S. A.: Towards an operational Ice Cloud Imager (ICI) retrieval product, Atmos. Meas. Tech., 13, 53–71, https://doi.org/10.5194/amt-13-53-2020, 2020. a
Field, P. R., Heymsfield, A. J., Detwiler, A. G., and Wilkinson, J. M.:
Normalized hail particle size distributions from the T-28 storm-penetrating
aircraft, J. Appl. Meteorol. Clim., 58, 231–245,
https://doi.org/10.1175/JAMC-D-18-0118.1, 2019. a, b, c
Forbes, R. M. and Tompkins, A. M.: An improved representation of cloud and
precipitation, ECMWF Newsletter No. 129, ECMWF, Reading, UK, 13–18, 2011. a
Forbes, R. M., Tompkins, A. M., and Untch, A.: A new prognostic bulk
microphysics scheme for the IFS, ECMWF Technical Memorandum, 649, https://doi.org/10.21957/bf6vjvxk, 2011. a
Forbes, R., Geer, A., Lonitz, K., and Ahlgrimm, M.: Reducing systematic errors in cold-air outbreaks, ECMWF newsletter, ECMWF, Reading, UK, 17–22, 2016. a
Fox, S.: An Evaluation of Radiative Transfer Simulations of Cloudy Scenes from a Numerical Weather Prediction Model at Sub-Millimetre Frequencies Using
Airborne Observations, Rem. Sens., 12, 2758,
https://doi.org/10.3390/rs12172758, 2020. a
Fox, S., Mendrok, J., Eriksson, P., Ekelund, R., O'Shea, S. J., Bower, K. N., Baran, A. J., Harlow, R. C., and Pickering, J. C.: Airborne validation of radiative transfer modelling of ice clouds at millimetre and sub-millimetre wavelengths, Atmos. Meas. Tech., 12, 1599–1617, https://doi.org/10.5194/amt-12-1599-2019, 2019. a, b, c
Fuchs, B. R. and Rutledge, S. A.: Investigation of lightning flash locations in isolated convection using LMA observations, J. Geophys. Res.-Atmos., 123, 6158–6174, https://doi.org/10.1002/2017JD027569, 2018. a
Geer, A. J. and Bauer, P.: Enhanced use of all-sky microwave observations
sensitive to water vapour, cloud and precipitation, Tech. Memo. 620, ECMWF,
Reading, UK, https://doi.org/10.21957/mi79jebka, 2010. a
Geer, A. J., Bauer, P., and Lopez, P.: Lessons learnt from the 1D+4D-Var
assimilation of rain and cloud affected SSM/I observations at ECMWF,
Published simultaneously as ECMWF Technical Memoranda 535 and
ECMWF/EUMETSAT fellowship reports 17,
https://doi.org/10.21957/spjjsd73m, 2007. a
Geer, A. J., Bauer, P., and O'Dell, C. W.: A revised cloud overlap scheme for
fast microwave radiative transfer, J. App. Meteor. Clim., 48, 2257–2270,
https://doi.org/10.1175/2009JAMC2170.1, 2009a. a, b, c, d
Geer, A. J., Forbes, R., and Bauer, P.: Cloud and precipitation overlap in
simplified scattering radiative transfer, EUMETSAT/ECMWF Fellowship
Programme Research Report 18, ECMWF, Reading, UK, 2009b. a
Geer, A., Ahlgrimm, M., Bechtold, P., Bonavita, M., Bormann, N., English, S.,
Fielding, M., Forbes, R., Robin Hogan, E. H., Janisková, M., Lonitz, K.,
Lopez, P., Matricardi, M., Sandu, I., and Weston, P.: Assimilating
observations sensitive to cloud and precipitation, Tech. Memo. 815, ECMWF,
Reading, UK, https://doi.org/10.21957/sz7cr1dym, 2017a. a, b, c, d, e, f
Geer, A. J., Baordo, F., Bormann, N., English, S., Kazumori, M., Lawrence, H., Lean, P., Lonitz, K., and Lupu, C.: The growing impact of satellite
observations sensitive to humidity, cloud and precipitation, Q. J. Roy.
Meteorol. Soc., 143, 3189–3206, https://doi.org/10.1002/qj.3172, 2017b. a, b
Geer, A. J., Lonitz, K., Weston, P., Kazumori, M., Okamoto, K., Zhu, Y., Liu,
E. H., Collard, A., Bell, W., Migliorini, S., Chambon, P., Fourrié, N.,
Kim, M.-J., Köpken-Watts, C., and Schraff, C.: All-sky satellite data
assimilation at operational weather forecasting centres, Q. J. Roy.
Meteorol. Soc., 144, 1191–1217, https://doi.org/10.1002/qj.3202, 2018. a, b
Geer, A. J., Migliorini, S., and Matricardi, M.: All-sky assimilation of infrared radiances sensitive to mid- and upper-tropospheric moisture and cloud, Atmos. Meas. Tech., 12, 4903–4929, https://doi.org/10.5194/amt-12-4903-2019, 2019. a, b, c
Geer, A. J., Bauer, P., Lonitz, K., Barlakas, V., Eriksson, P., Mendrok, J., Doherty, A., Hocking, J., and Chambon, P.: Bulk hydrometeor optical properties for microwave and sub-mm radiative transfer in RTTOV-SCATT v13.0, Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2021-73, in review, 2021. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s
Guerbette, J., Mahfouf, J.-F., and Plu, M.: Towards the assimilation of all-sky microwave radiances from the SAPHIR humidity sounder in a limited area NWP model over tropical regions, Tellus A, 68, 28620,
https://doi.org/10.3402/tellusa.v68.28620, 2016. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D.,
Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P.,
Biavati, G., Bidlot, J., Bonavita, M., Chiara, G. D., Dahlgren, P., Dee, D.,
Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer,
A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M.,
Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P.,
Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.: The ERA5 global
reanalysis, Q. J. Roy. Meteorol. Soc., 146, 1999–2049,
https://doi.org/10.1002/qj.3803, 2020. a
Hogan, R. J. and Illingworth, A. J.: Deriving cloud overlap statistics from
radar, Q. J. Roy. Meteorol. Soc., 126, 2903–2909,
https://doi.org/10.1002/qj.49712656914, 2000. a
Hong, G., Heygster, G., Miao, J., and Kunzi, K.: Detection of tropical deep
convective clouds from AMSU-B water vapor channels measurements, J.
Geophys. Res., 110, D05205, https://doi.org/10.1029/2004JD004949, 2005. a, b
Joseph, J., Wiscombe, W. J., and Weinman, J. A.: The delta-Eddington
approximation for radiative flux transfer, J. Atmos. Sci., 33, 2452–2459,
https://doi.org/10.1175/1520-0469(1976)033<2452:TDEAFR>2.0.CO;2,
1976. a
Kazumori, M., Geer, A. J., and English, S. J.: Effects of all-sky assimilation of GCOM-W/AMSR2 radiances in the ECMWF numerical weather prediction system, Q. J. Roy. Meteorol. Soc., 142, 721–737, https://doi.org/10.1002/qj.2669, 2016. a
Klein, S. A., McCoy, R. B., Morrison, H., Ackerman, A. S., Avramov, A.,
de Boer, G., Chen, M., Cole, J. N. S., Del Genio, A. D., Falk, M., Foster,
M. J., Fridlind, A., Golaz, J.-C., Hashino, T., Harrington, J. Y., Hoose, C.,
Khairoutdinov, M. F., Larson, V. E., Liu, X., Luo, Y., McFarquhar, G. M.,
Menon, S., Neggers, R. A. J., Park, S., Poellot, M. R., Schmidt, J. M.,
Sednev, I., Shipway, B. J., Shupe, M. D., Spangenberg, D. A., Sud, Y. C.,
Turner, D. D., Veron, D. E., von Salzen, K., Walker, G. K., Wang, Z., Wolf,
A. B., Xie, S., Xu, K.-M., Yang, F., and Zhang, G.: Intercomparison of model
simulations of mixed-phase clouds observed during the ARM Mixed-Phase
Arctic Cloud Experiment. I: single-layer cloud, Q. J. Roy.
Meteorol. Soc., 135, 979–1002,
https://doi.org/10.1002/qj.416, 2009. a
Kneifel, S., Dias Neto, J., Ori, D., Moisseev, D., Tyynelä, J., Adams,
I. S., Kuo, K.-S., Bennartz, R., Berne, A., Clothiaux, E. E., Eriksson, P.,
Geer, A. J., Honeyager, R., Leinonen, J., and Westbrook, C. D.: Summer
snowfall workshop: Scattering properties of realistic frozen hydrometeors
from simulations and observations, as well as defining a new standard for
scattering databases, Bull. Amer. Meteorol. Soc., 99, ES55–ES58,
https://doi.org/10.1175/BAMS-D-17-0208.1, 2018. a
Kotsuki, S., Sato, Y., and Miyoshi, T.: Data Assimilation for Climate Research: Model Parameter Estimation of Large-Scale Condensation Scheme, J. Geophys. Res.-Atmos., 125, e2019JD031304,
https://doi.org/10.1029/2019JD031304, 2020. a, b
Kulie, M. S., Bennartz, R., Greenwald, T. J., Chen, Y., and Weng, F.:
Uncertainties in microwave properties of frozen precipitation: implications
for remote sensing and data assimilation, J. Atmos. Sci., 67, 3471–3487,
https://doi.org/10.1175/2010JAS3520.1, 2010. a, b
Kullback, S. and Leibler, R. A.: On information and sufficiency, Ann. Math. Stat., 22, 79–86, https://doi.org/10.1214/aoms/1177729694, 1951. a
Kumjian, M. R., Ganson, S. M., and Ryzhkov, A. V.: Freezing of raindrops in
deep convective updrafts: A microphysical and polarimetric model, J. Atmos.
Sci., 69, 3471–3490, https://doi.org/10.1175/JAS-D-12-067.1,
2012. a, b
Kummerow, C.: On the accuracy of the Eddington approximation for radiative
transfer in the microwave frequencies, J. Geophys. Res., 98, 2757–2765,
https://doi.org/10.1029/92JD02472, 1993. a
Kummerow, C., Hong, Y., Olson, W., Yang, S., Adler, R., McCollum, J., Ferraro, R., Petty, G., Shin, D.-B., and Wilheit, T.: The evolution of the Goddard Profiling Algorithm (GPROF) for rainfall estimation from passive microwave sensors, J. Appl. Meteorol., 40, 1801–1820,
https://doi.org/10.1175/1520-0450(2001)040<1801:TEOTGP>2.0.CO;2,
2001. a
Kunkee, D., Poe, G., Boucher, D., Swadley, S., Hong, Y., Wessel, J., and
Uliana, E.: Design and evaluation of the first Special Sensor Microwave
Imager/Sounder, IEEE Trans. Geosci. Remote Sensing, 46, 863–883,
https://doi.org/10.1109/TGRS.2008.917980, 2008. a, b
Locatelli, J. D. and Hobbs, P. V.: Fall speeds and masses of solid
precipitation particles, J. Geophys. Res., 79, 2185–2197,
https://doi.org/10.1029/JC079i015p02185, 1974. a
McFarquhar, G. M. and Heymsfield, A. J.: Parameterization of tropical cirrus
ice crystal size distributions and implications for radiative transfer:
Results from CEPEX, J. Atmos. Sci., 54, 2187–2200,
https://doi.org/10.1175/1520-0469(1997)054<2187:POTCIC>2.0.CO;2,
1997. a, b, c
Morrison, H., van Lier-Walqui, M., Fridlind, A. M., Grabowski, W. W.,
Harrington, J. Y., Hoose, C., Korolev, A., Kumjian, M. R., Milbrandt, J. A.,
Pawlowska, H., Posselt, D. J., Prat, O. P., Reimel, K. J., Shima, S.-I., van Diedenhoven, B., and Xue, L.: Confronting the challenge of modeling cloud and precipitation microphysics, J. Adv. Model. Earth Sys., 12, e2019MS001689, https://doi.org/10.1029/2019MS001689, 2020. a
Norris, P. M. and Da Silva, A. M.: Assimilation of satellite cloud data into
the GMAO finite-volume data assimilation system using a parameter
estimation method. Part I: Motivation and algorithm description, J. Atmos.
Sci., 64, 3880–3895, https://doi.org/10.1175/2006JAS2046.1,
2007. a, b
Ollinaho, P., Bechtold, P., Leutbecher, M., Laine, M., Solonen, A., Haario, H., and Järvinen, H.: Parameter variations in prediction skill optimization at ECMWF, Nonlin. Processes Geophys., 20, 1001–1010, https://doi.org/10.5194/npg-20-1001-2013, 2013. a
Petty, G. W. and Huang, W.: The modified gamma size distribution applied to
inhomogeneous and nonspherical particles: Key relationships and
conversions, J. Atmos. Sci., 68, 1460–1473,
https://doi.org/10.1175/2011JAS3645.1, 2011. a
Posselt, D. J.: A Bayesian examination of deep convective squall-line
sensitivity to changes in cloud microphysical parameters, J. Atmos. Sci., 73,
637–665, https://doi.org/10.1175/JAS-D-15-0159.1, 2016. a, b, c
Posselt, D. J. and Bishop, C. H.: Nonlinear parameter estimation: Comparison of an ensemble Kalman smoother with a Markov chain Monte Carlo algorithm,
Mon. Weather Rev., 140, 1957–1974,
https://doi.org/10.1175/MWR-D-11-00242.1, 2012. a, b, c, d
Posselt, D. J. and Vukicevic, T.: Robust Characterization of Model Physics
Uncertainty for Simulations of Deep Moist Convection, Mon. Weath. Rev., 138,
1513–1535, https://doi.org/10.1175/2009MWR3094.1, 2010. a
Rabier, F., Järvinen, H., Klinker, E., Mahfouf, J.-F., and Simmons, A.: The ECMWF operational implementation of four-dimensional variational
assimilation. I: Experimental results with simplified physics, Q. J.
Roy. Meteorol. Soc., 126, 1148–1170,
https://doi.org/10.1002/qj.49712656415, 2000. a
Rasp, S., Pritchard, M. S., and Gentine, P.: Deep learning to represent subgrid processes in climate models, Proc. Nat. Acad. Sci., 115, 9684–9689,
https://doi.org/10.1073/pnas.1810286115, 2018. a
Ruckstuhl, Y. and Janjić, T.: Parameter and state estimation with ensemble Kalman filter based algorithms for convective-scale applications, Q. J. Roy. Meteorol. Soc., 144, 826–841,
https://doi.org/10.1002/qj.3257, 2018. a
Ruckstuhl, Y. and Janjić, T.: Combined state-parameter estimation with the LETKF for convective-scale weather forecasting, Mon. Weather Rev., 148,
1607–1628, https://doi.org/10.1175/MWR-D-19-0233.1, 2020. a, b
Ruiz, J. and Pulido, M.: Parameter estimation using ensemble-based data
assimilation in the presence of model error, Mon. Weath. Rev., 143,
1568–1582, https://doi.org/10.1175/MWR-D-14-00017.1, 2015. a
Ruiz, J. J., Pulido, M., and Miyoshi, T.: Estimating model parameters with
ensemble-based data assimilation: A review, J. Meteorol. Soc. Jpn. Ser. II,
91, 79–99, https://doi.org/10.2151/jmsj.2013-201, 2013. a
Saunders, R., Hocking, J., Turner, E., Rayer, P., Rundle, D., Brunel, P., Vidot, J., Roquet, P., Matricardi, M., Geer, A., Bormann, N., and Lupu, C.: An update on the RTTOV fast radiative transfer model (currently at version 12), Geosci. Model Dev., 11, 2717–2737, https://doi.org/10.5194/gmd-11-2717-2018, 2018. a, b
Saunders, R., Hocking, J., Turner, E., Havemann, S., Geer, A., Lupu, C., Vidot, J., Chambon, P., Köpken-Watts, C., Scheck, L., Stiller, O., Stumpf, C., Borbas, E., and Brunel, P.: RTTOV-13 science and validation report, NWP-SAF report NWPSAF-MO-TV-046, EUMETSAT NWP-SAF, Met Office, Exeter, UK, 2020. a, b, c
Schneider, T., Lan, S., Stuart, A., and Teixeira, J.: Earth system modeling
2.0: A blueprint for models that learn from observations and targeted
high-resolution simulations, Geophys. Res. Lett., 44, 12–396,
https://doi.org/10.1002/2017GL076101, 2017. a, b
Sieron, S. B., Clothiaux, E. E., Zhang, F., Lu, Y., and Otkin, J. A.:
Comparison of using distribution-specific versus effective radius methods for
hydrometeor single-scattering properties for all-sky microwave satellite
radiance simulations with different microphysics parameterization schemes, J.
Geophys. Res.: Atmos., 122, 7027–7046,
https://doi.org/10.1002/2017JD026494, 2017JD026494, 2017.
a
Tiedtke, M.: A comprehensive mass flux scheme for cumulus parameterization in
large-scale models, Mon. Weather Rev., 117, 1779–1800,
https://doi.org/10.1175/1520-0493(1989)117<1779:ACMFSF>2.0.CO;2,
1989. a, b
Tiedtke, M.: Representation of clouds in large-scale models, Mon. Weather Rev., 128, 1070–1088, https://doi.org/10.1175/1520-0493(1993)121<3040:ROCILS>2.0.CO;2,
1993. a
Tompkins, A. M., Gierens, K., and Rädel, G.: Ice supersaturation in the
ECMWF integrated forecast system, Q. J. Roy. Meteorol. Soc., 133,
53–63, https://doi.org/10.1002/qj.14, 2007. a
Varble, A., Zipser, E. J., Fridlind, A. M., Zhu, P., Ackerman, A. S.,
Chaboureau, J.-P., Collis, S., Fan, J., Hill, A., and Shipway, B.: Evaluation
of cloud-resolving and limited area model intercomparison simulations using
TWP-ICE observations: 1. Deep convective updraft properties, J. Geophys.
Res.-Atmos., 119, 13–891,
https://doi.org/10.1002/2013JD021371, 2014. a
Yu, L. and O'Brien, J. J.: Variational estimation of the wind stress drag
coefficient and the oceanic eddy viscosity profile, J. Phys. Ocean., 21,
709–719, https://doi.org/10.1175/1520-0485(1991)021<0709:VEOTWS>2.0.CO;2, 1991. a
Zelinka, M. D., Myers, T. A., McCoy, D. T., Po-Chedley, S., Caldwell, P. M.,
Ceppi, P., Klein, S. A., and Taylor, K. E.: Causes of Higher Climate
Sensitivity in CMIP6 Models, Geophys. Res. Lett., 47,
e2019GL085782, https://doi.org/10.1029/2019GL085782, 2020. a
Zhu, Y. and Navon, I.: Impact of parameter estimation on the performance of the FSU global spectral model using its full-physics adjoint, Mon. Weather Rev., 127, 1497–1517, https://doi.org/10.1175/1520-0493(1999)127<1497:IOPEOT>2.0.CO;2,
1999. a
Zipser, E. J., Cecil, D. J., Liu, C., Nesbitt, S. W., and Yorty, D. P.: Where
are the most intense thunderstorms on Earth?, Bull. Am. Meteorol. Soc., 87,
1057–1072, https://doi.org/10.1175/BAMS-87-8-1057, 2006. a, b
Short summary
Satellite observations sensitive to cloud and precipitation help improve the quality of weather forecasts. However, they are sensitive to things that models do not forecast, such as the shapes and sizes of snow and ice particles. These details can be estimated from the observations themselves and then incorporated in the satellite simulators used in weather forecasting. This approach, known as parameter estimation, will be increasingly useful to build models of poorly known physical processes.
Satellite observations sensitive to cloud and precipitation help improve the quality of weather...