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GUO Jianping, CHEN Tianmeng, CHENG Xiaoping, TIAN Weihong, YOU Wei, DANG Ruijun, GUO Xiaoran, WU Jingyan, LI Ning, ZHANG Zhen, SUN Yuping. 2023: Progress in targeted observation for meso-scale convective system and some thoughts on its applications to convection nowcasting in large cities. Torrential Rain and Disasters, 42(6): 613-627. DOI: 10.12406/byzh.2023-035
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Improving the forecasting skills of the meso-scale convective system (MCS) is one of the key scientific problems in the field of numerical weather prediction. The occurrence and development of severe convective weather are affected by multiple factors such as atmospheric thermodynamic and kinetic conditions, topography, and air pollution conditions. Due largely to the uncertainty in models and the inevitable errors of initial values, large uncertainties still exist for the accurate prediction of severe weather produced by the MCS. Therefore, to effectively improve the accuracy of severe convective weather forecasts in China, conducting targeted observation experiments in key areas of interest, which typically helps to reduce the uncertainty level of the model's initial meteorological field, may be one of the effective ways forward. It follows that the initiation and formation mechanisms of MCS can be revealed, and the forecast skills of severe convection will be improved. In this paper, we first propose the technology roadmap of targeted observation as follows. Based on the modern meteorological observational network over the Jing-Jin-Ji area, the typical MCS forecast sensitive regions are identified by using the Ensemble Transform Kalman filter, combined with the atmospheric sounding systems mounted on the mobile vehicle, where targeted observation experiments will be conducted. As such, convective initiation mechanisms are elucidated, and the novel methods expected to improve the forecast skill for MCS are explored. Secondly, in response to the challenge of "finding needles in a haystack" in the vertical observation of the lower atmosphere for short-term forecasting and warning of severe convective weather in large cities, the potential application value of a dynamic triangular observation mesonet in the study of triggering and development mechanisms of severe convective weather was explored through the construction of a Pyramid-shaped LOwer Tropospheric Observational System (PLOTOS) that consists of five stations with simultaneous vertical observation capabilities. Finally, it is suggested that the initiation and formation mechanisms of severe convective weather be unraveled using PLOTOS, and the mesoscale targeted observation technology be developed, which is crucial to the improvement of weather observational networks in large cities and provide new ideas and methods for improving the forecast of severe convective weather processes.
|
范爱芬, 彭霞云, 娄小芬, 等. 2022. 秋季台风倒槽特大暴雨的对流特征和预报难点分析[J]. 暴雨灾害, 41(3): 298-307. doi: 10.3969/j.issn.1004-9045.2022.03.006
Fan A F, Peng X Y, Lou X F et al. 2022. Analyses of the convection character and forecast difficulty of autumn typhoon inverted trough heavy downpour[J]. Torrential RainandDisasters, 41(3): 298-307(inChinese). doi: 10.3969/j.issn.1004-9045.2022.03.006
|
|
雷荔傈, 谈哲敏. 2008. 适应性观测及其策略问题[J]. 气象科学, 28(1): 109-118. doi: 10.3969/j.issn.1009-0827.2008.01.019
Lei L L, Tan Z M. 2008. On adaptive observation and adaptive observation strategies[J]. Journal of the Meteorological Sciences, 28(1): 109-118 (in Chinese). doi: 10.3969/j.issn.1009-0827.2008.01.019
|
|
刘梦娟, 杨引明. 2017. 基于风廓线网的散度和涡度计算[J]. 气象科技进展, 7(1): 27-32. doi: 10.3969/j.issn.2095-1973.2017.01.004
Liu M J, Yang Y M. 2017. Calculation of horizontal divergence and vorticity using wind profiler network[J]. Advances in Meteorological Science and Technology, 7(1): 27-32 (in Chinese). doi: 10.3969/j.issn.2095-1973.2017.01.004
|
|
马旭林. 2008. 基于集合卡尔曼变换(ETKF)理论的适应性观测研究与应用[D]. 南京: 南京信息工程大学.
Ma X L. 2008. Study on the ensemble transform kalman filter-based adaptive observation and applications[D]. Nanjing: Nanjing University of Information Science and Technology. doi: 10.7666/d.y1257794
|
|
穆穆. 2013. 目标观测的方法、现状与发展展望[J]. 中国科学: 地球科学, 43(11): 1717-1725.
Mu M. Present situation and development prospect of objective observation[J]. Scientia Sinica Terrae, 43(11): 1717-1725(in Chinese). doi: 10.1360/zd-2013-43-11-1717
|
|
闵锦忠, 吴乃庚. 2020. 近二十年来暴雨和强对流可预报性研究进展[J]. 大气科学, 44(5): 1039-1056. doi: 10.3878/j.issn.1006-9895.2003.19186
Min J Z, Wu N G. 2020. Advances in atmospheric predictability of heavy rain and severe convection[J]. Chinese Journal of Atmospheric Sciences, 44(5): 1039-1056 (in Chinese). doi: 10.3878/j.issn.1006-9895.2003.19186
|
|
田伟红. 2006. 集合变换卡尔曼滤波方法在集合预报和适应性观测中的初步应用[D]. 北京: 中国气象科学研究院.
Tian W H. 2006. Collection of transform kalman filter method and adaptive observation on collection forecast the preliminary application[D]. Beijing: Chinese Academy of Meteorological Sciences
|
|
王佳, 陈耀登. 2019. 观测资料在华东区域数值预报中的敏感性研究[D]. 南京: 南京信息工程大学.
Wang J, Chen Y D. 2019. Sensitivity analysis of observation data in numerical weather prediction over east China[D]. Nanjing: Nanjing University of Information Science and Technology. doi: 10.27248/d.cnki.gnjqc.2019.000212
|
|
王璐璐, 贾浩松, 刘静. 2023. 一次由边界层冷空气引发的豫南对流性暴雨成因分析[J]. 暴雨灾害, 42(2): 134-143. doi: 10.12406/byzh.2021-246
Wang L L, Jia H S, Liu J. 2023. Analysis of a convective rainstorm in southern Henan caused by the cold air from the boundary layer[J]. Torrential Rain and Disasters, 42(2): 134-143 (in Chinese). doi: 10.12406/byzh.2021-246
|
|
王小龙, 王彤, 李映春, 等. 2022. 基于"葵花8号"气象卫星的陇东南地区强对流识别跟踪技术研究[J]. 沙漠与绿洲气象, 16(5): 56-61. doi: 10.12057/j.issn.1002-0799.2022.05.008
Wang X L, Wang T, Li Y C et al. 2022. Research on identification and tracking technology of severe convection in Southeast Gansu based on himawari-8 meteorological satellite[J]. Desert and Oasis Meteorology, 16(5): 56-61 (in Chinese). doi: 10.12057/j.issn.1002-0799.2022.05.008
|
|
张宇, 陈德辉, 薛纪善, 等. 2012. 湿度因子对适应性观测敏感区估算的影响研究[J]. 气象学报, 70(1): 91-100. doi: 10.11676/qxxb2012.008
Zhang Y, Chen D H, Xue J S, et al. 2012. Study of the influence of the humidity factor on estimation in the adaptive observation sensitive region[J]. Acta Meteorologica Sinica, 70(1): 91-100 (in Chinese). doi: 10.11676/qxxb2012.008
|
|
Adler B, Kalthoff N, Kohler M, et al. 2016. The variability of water vapour and pre-convective conditions over the mountainous island of Corsica. Quarterly Journal of the Royal Meteorological Society, 142: 335-346. doi: 10.1002/qj.2545
|
|
Ancell B, Hakim G J. 2007. Comparing adjoint and ensemble-sensitivity analysis with applications to observation targeting[J]. Monthly Weather Review, 135(12): 4117-4134. doi: 10.1175/2007MWR1904.1
|
|
Arnold C P Jr, Dey C H. 1986. Observing-systems simulation experiments: Past, present, and future[J]. Bulletin of the American Meteorological Society, 67(6): 687-695. doi: 10.1175/1520-0477(1986)067<0687:OSSEPP>2.0.CO;2
|
|
Baker N L, Daley R. 2007. Observation and background adjoint sensitivity in the adaptive observation-targeting problem[J]. Quarterly Journal of the Royal Meteorological Society, 126(565): 1431-1454. doi: 10.1256/smsqj.56510
|
|
Barthlott C, Schipper J W, Kalthoff N, et al. 2010. Model representation of boundary-layer convergence triggering deep convection over complex terrain: A case study from COPS[J]. Atmospheric Research, 95(2-3): 172-185. doi: 10.1016/j.atmosres.2009.09.010
|
|
Behrendt A, Pal S, Aoshima F, et al. 2011. Observation of convection initiation processes with a suite of state-of-the-art research instruments during COPS IOP 8b[J]. Quarterly Journal of the Royal Meteorological Society, 137(S1): 81-100. doi: 10.1002/qj.758
|
|
Bellamy J C. 1949. Objective calculations of divergence, vertical velocity, and vorticity[J]. Bulletin of the American Meteorological Society, 30(2): 45-49. doi: 10.1175/1520-0477-30.2.45
|
|
Benjamin S G, Schwartz B E, Szoke E J, et al. 2004. The value of wind profiler data in U.S. weather forecasting[J]. Bulletin of the American Meteorological Society, 85(12): 871-1886. doi: 10.1175/BAMS-85-12-1871
|
|
Benjamin S G, Jamison B D, Moninger W R, et al. 2010. Relative short-range forecast impact from aircraft, profiler, radiosonde, VAD, GPS-PW, METAR, and mesonet observations via the RUC hourly assimilation cycle[J]. Monthly Weather Review, 138(4): 1319-1343. doi: 10.1175/2009MWR3097
|
|
Bishop C H, Toth Z. 1999. Ensemble transformation and adaptive observations[J]. Journal of the atmospheric sciences, 56(11): 1748-1765. doi: 10.1175/1520-0469(1999)056<1748:ETAAO>2.0.CO;2
|
|
Bishop C H, Etherton B J, Majumdar S J. 2001. Adaptive sampling with the ensemble transform kalman filter. Part Ⅰ: Theoretical aspects[J]. Monthly Weather Review, 129(3): 420-436. doi: 10.1175/1520-0493(2001)129<0420:ASWTET>2.0.CO;2
|
|
Burpee R W, Franklin J L, Lord S J, et al. 1996. The impact of Omega dropwindsondes on operational hurricane track forecast models[J]. Bulletin of the American Meteorological Society, 77(5): 925-933. doi: 10.1175/1520-0477(1996)077<0925:TIOODO>2.0.CO;2
|
|
Byers H R, Rodebush H R. 1948. Causes of thunderstorms of the florida peninsula[J]. Journal of Atmospheric Sciences, 5(5): 275-280. doi: 10.1175/1520-0469(1948)005<0275:COTOTF>2.0.CO;2
|
|
Chen D D, Guo J P, Yao D, et al. 2020. Elucidating the life cycle of warm-season mesoscale convective systems in eastern China from the Himawari-8 geostationary satellite[J]. Remote Sensing, 12(14): 2307. doi: 10.3390/rs12142307
|
|
Coniglio M C, Hitchcock S M, Knopfmeier K H. 2016. Impact of assimilating preconvective upsonde observations on short-term forecasts of convection observed during MPEX[J]. Monthly Weather Review, 144(11): 4301-4325. doi: 10.1175/MWR-D-16-0091.1
|
|
Cunning J B. 1986. The oklahoma-kansas preliminary regional experiment for STORM-Central[J]. Bulletin of the American Meteorological Society, 67(12): 1478-1486. doi: 10.1175/1520-0477(1986)067<1478:TOKPRE>2.0.CO;2
|
|
Donner L J, Phillips V T. 2003. Boundary layer control on convective available potential energy: Implications for cumulus parameterization[J]. Journal of Geophysical Research: Atmospheres, 108(D22): 4701. doi: 10.1029/2003JD003773
|
|
Fabry F. 2010. For how long should what data be assimilated for the mesoscale forecasting of convection and why? Part Ⅱ: On the observation signal from different sensors[J]. Monthly Weather Review, 138(1): 256-264. doi: 10.1175/2009MWR2884.1
|
|
Gelaro R, Zhu Y. 2009. Examination of observation impacts derived from observing system experiments (OSEs) and adjoint models[J]. Tellus A:Dynamic Meteorology and Oceanography,61(2):179-193. doi: 10.1111/j.1600-0870.2008.00388.x
|
|
Gravelle C M, Mecikalski J R, Line W E, et al. 2016. Demonstration of a GOES-R satellite convective toolkit to bridge the gap between severe weather watches and warnings:An example from the 20 May 2013 Moore, Oklahoma, tornado outbreak[J]. Bulletin of the American Meteorological Society,97(1):69-84. doi: 10.1175/BAMS-D-14-00054.1
|
|
Groenemeijer P, Barthlott C, Behrendt A, et al. 2009. Observations of Kinematics and thermodynamic structure surrounding a convective storm cluster over a low mountain range[J]. Monthly Weather Review, 137(2): 585-602. doi: 10.1175/2008MWR2562.1
|
|
Guo J P, Su T N, Li Z Q, et al. 2017. Declining frequency of summertime local-scale precipitation over eastern China from 1970 to 2010 and its potential link to aerosols[J]. Geophysical Research Letters, 44(11): 5700-5708. doi: 10.1002/2017GL073533
|
|
Guo X R, Guo J P, Zhang D L, et al. 2023. Vertical divergence profiles as detected by two wind profiler mesonets over East China: implications for nowcasting convective storms[J]. Quarterly Journal of the Royal Meteorological Society, 149(754): 1629-1649. doi: 10.1002/qj.4474
|
|
Hamill T M, Snyder C. 2002. Using improved background-error covariances from an ensemble Kalman filter for adaptive observations[J]. Monthly weather review, 130(6): 1552-1572. doi: 10.1175/1520-0493(2002)130<1552:UIBECF>2.0.CO;2
|
|
Hitchcock S M, Schumacher R S, Herman G R, et al. 2019. Evolution of preand postconvective environmental profiles from mesoscale convective systems during PECAN[J]. Monthly Weather Review, 147(7): 2329-2354. doi: 10.1175/WAF-D-17-0088.1.
|
|
Joly A, Browning K A, Bessemoulin P, et al. 1999. Overview of the field phase of the fronts and Atlantic Storm-Track EXperiment(FASTEX) project[J]. Quarterly Journal of the Royal Meteorological Society, 125(561): 3131-3163. doi: 10.1002/qj.49712556103
|
|
Joo S, Eyre J, Marriott R. 2013. The impact of MetOp and other satellite data within the Met Office global NWP system using an adjoint-based sensitivity method[J]. Monthly Weather Review, 141(10): 3331-3342. doi: 10.1175/MWR-D-12-00232.1
|
|
Jung B, Kim H M, Auligné T, et al. 2013. Adjoint-derived observation impact using WRF in the western North Pacific[J]. Monthly Weather Review, 141(11): 4080-4097. doi: 10.1175/MWR-D-12-00197.1
|
|
Klein C, Belusic D, Taylor C M. 2018. Wavelet scale analysis of mesoscale convective systems for detecting deep convection from infrared imagery[J]. Journal of Geophysical Research: Atmospheres, 123(6): 3035-3050. doi: 10.1002/2017JD027432
|
|
Kumjian M R, Ryzhkov A V. 2008. Polarimetric signatures in supercell thunderstorms[J]. Journal of applied meteorology and climatology, 47(7): 1940-1961. doi: 10.1175/2007JAMC1874.1
|
|
Langland R H, Baker N L. 2004. Estimation of observation impact using the NRL atmospheric variational data assimilation adjoint system[J]. Tellus A: Dynamic Meteorology and Oceanography, 56(3): 189-201. doi: 10.1111/j.1600-0870.2004.00056.x
|
|
Langland R H, Gelaro R, Rohaly G D, et al. 1999. Targeted observations in FASTEX: Adjoint-based targeting procedures and data impact experiments in IOPs-17 and 18[J]. Quarterly Journal of the Royal Meteorological Society, 125(561): 3241-3270. doi: 10.1002/qj.49712556107
|
|
Lee Y H, Choi Y, Ghim Y S. 2016. Classification of diurnal patterns of particulate inorganic ions downwind of metropolitan Seoul[J]. Environmental Science and Pollution Research, 23(9): 8917-8928. doi: 10.1007/s11356-016-6125-3
|
|
Lorenc A C, Marriott R T. 2014. Forecast sensitivity to observations in the Met Office Global numerical weather prediction system[J]. Quarterly Journal of the Royal Meteorological Society, 140(678): 209-224. doi: 10.1002/qj.2122
|
|
Majumdar S J. 2016. A review of targeted observations[J]. Bulletin of the American Meteorological Society, 97(12): 2287-2303. doi: 10.1175/BAMS-D-14-00259.1
|
|
Majumdar S J, Aberson S D, Bishop C H, et al. 2011. Targeted observations for improving numerical weather prediction: an overview[M]. A World Weather Research Programme/THORPEX Publication: 15, 37
|
|
Mallick S, Dutta D, Min K H. 2017. Quality assessment and forecast sensitivity of global remote sensing observations[J]. Advances in Atmospheric Sciences, 34(3): 371-382. doi: 10.1007/s00376-016-6109-8
|
|
Mansfield D, Richardson D, Truscott B. 2005. An overview of the Atlantic THORPEX Regional campaign[C]//in Proceedings of the First THORPEX International Science Symposium, Montreal, Canada: 91-94
|
|
Mecikalski J R, Bedka K M. 2006. Forecasting convective initiation by monitoring the evolution of moving cumulus in daytime GOES imagery[J]. Monthly Weather Review, 134(1): 49-78. doi: 10.1175/MWR3062.1
|
|
Mecikalski J R, Bedka K M, Paech S J, et al. 2008. A statistical evaluation of GOES cloud-top properties for nowcasting convective initiation[J]. Monthly Weather Review, 136(12): 4899-4914. doi: 10.1175/2008MWR2352.1
|
|
Medeiros B, Hall A, Stevens B. 2005. What controls the mean depth of the PBL?[J]. JournalofClimate, 18(16): 3157-3172. doi: 10.1175/JCLI3417.1
|
|
Min M, Wu C Q, Li C, et al. 2017. Developing the science product algorithm testbed for Chinese next-generation geostationary meteorological satellites:Fengyun-4 series[J]. Journal of the Atmospheric Sciences,31(4):708-719. doi: 10.1007/s13351-017-6161-z
|
|
Mu M, Duan W S, Wang B. 2003. Conditional nonlinear optimal perturbation and its applications[J]. Nonlinear Processes in Geophysics,10(6):493-501. doi: 10.5194/npg-10-493-2003
|
|
Parker M D. 2014. Composite VORTEX2 supercell environments from near-storm soundings[J]. Monthly Weather Review, 142(2): 508-529. doi: 10.1175/MWR-D-13-00167.1
|
|
Palmer T N, Gelaro R, Barkmeijer J, et al. 1998. Singular vectors, metrics, and adaptive observations[J]. Journal of the Atmospheric Sciences, 55(4): 633-653. doi: 0.1175/1520-0469(1998)055<0633:SVMAAO>2.0.CO;2
|
|
Parsons D B, Beland M, Burridge D, et al. 2017. Thorpex research and the science of prediction[J]. Bulletin of the American Meteorological Society, 98(4): 807-830. doi: 10.1175/BAMS-D-14-00025.1
|
|
Rabier F, Gauthier P, Cardinali C, et al. 2008. An update on THORPEX-related research in data assimilation and observing strategies[J]. Nonlinear Processes in Geophysics, 15(1): 81-94. doi: 10.5194/npg-15-81-2008
|
|
Rabier F, Klinker E, Courtier P, et al. 1996. Sensitivity of forecast errors to initial conditions[J]. Quarterly Journal of the Royal Meteorological Society, 122(529): 121-150. doi: 10.1002/qj.49712252906
|
|
Roebber P J, Swanson K L, Ghorai J K. 2008. Synoptic control of mesoscale precipitating systems in the Pacific Northwest[J]. Monthly Weather Review, 136(9): 3465-3476. doi: 10.1175/2008MWR2264.1
|
|
Schumacher R S. 2011. Ensemble-based analysis of factors leading to the development of a multiday warm-season heavy rain event[J]. Monthly WeatherReview, 139(9): 3016-3035. doi: 10.1175/MWR-D-10-05022.1
|
|
Snyder C. 1996. Summary of an informal workshopon adaptive observations and FASTEX[J]. Bulletin of the American Meteorological Society, 77(5): 953-961. doi: 10.1175/1520-0477-77.5.953
|
|
Szunyogh I, Toth Z, Morss R E, et al. 2000. The effect of targeted dropsonde observations during the 1999 Winter Storm Reconnaissance Program[J]. Monthly Weather Review, 128(10): 3520-3537. doi: 10.1175/1520-0493(2000)128<3520:TEOTDO>2.0.CO;2
|
|
Trapp R J, Stensrud D J, Coniglio M C, et al. 2016. Mobile Radiosonde Deployments during the Mesoscale Predictability Experiment (MPEX): rapid and adaptive sampling of upscale convective feedbacks[J]. Bulletin of the American Meteorological Society, 97(3): 329-336. doi: 10.1175/BAMS-D-14-00258.1
|
|
Trenberth K E, Dai A G, Rasmussen R M, et al. 2003. The changing character of precipitation[J]. Bulletin of the American Meteorological Society, 84(9): 1205-1218. doi: 10.1175/BAMS-84-9-1205
|
|
Wandishin, Matthew S, David J, et al. 2008. On the predictability of mesoscale convective systems: Two-dimensional simulations[J]. Weather and Forecasting, 23(5): 773-785. doi: 10.1175/2008WAF2007057.1
|
|
Weckwerth T M, Parsons D B, Koch S E, et al. 2004. An overview of the International H2O Project (IHOP_2002) and some preliminary highlights[J]. Bulletin of the American Meteorological Society, 85(2): 253-277. doi: 10.1175/BAMS-85-2-253
|
|
Weisman M L, Trapp R J, Romine G S, et al. 2015. The Mesoscale Predictability Experiment (MPEX)[J]. Bulletin of the American Meteorological Society, 96(12): 2127-2149. doi: 10.1175/BAMS-D-13-00281.1
|
|
Weygandt S S, Loughe A F, Benjamin S G, et al. 2004. Scale sensitivities in model precipitation skill scores during IHOP[C]//Proceedings of 22th Conference on Severe Local Storms, Hyannis, MA.
|
|
Wilson J W, Feng Y R, Chen M, et al. 2010. Nowcasting challenges during the beijing olympics: successes, failures, and implications for future nowcasting systems[J]. Weather and Forecasting, 25(6): 1691-1714. doi: 10.1175/2010WAF2222417.1
|
|
Wilson J W, Schreiber W E. 1986. Initiation of convective storms at radar-observed boundary-layer convergence lines[J]. Monthly Weather Review, 114(12): 2516-2536. doi: 10.1175/1520-0493(1986)114<2516:IOCSAR>2.0.CO;2
|
|
Wu Y Z, Guo J P, Chen T M, et al. 2023. Forecasting precipitation from radar wind profiler and reanalysis using the random forest algorithm[J]. Remote Sensing, 15(6): 1635. doi: 10.3390/rs15061635
|
|
Yan Y, Miao Y C, Guo J P, et al. 2019. Synoptic patterns and sounding-derived parameters associated with summertime heavy rainfall in Beijing[J]. International Journal of Climatology, 39: 1476-1489. doi: 10.1002/joc.5895
|
|
Zhang H, Sharma G, Dhawan S, et al. 2020. Comparison of discrete, discrete-sectional, modal and moment models for aerosol dynamics simulations[J]. Aerosol Science and Technology, 54(7): 739-760. doi: 10.1080/02786826.2020.1723787
|
|
Zheng Z F, Zhao C, Lolli S, et al. 2020. Diurnal variation of summer precipitation modulated by air pollution: observational evidences in the Beijing metropolitan area[J]. Environmental Research Letters, 15(9): 094053. doi: 10.1088/1748-9326/ab99fc
|
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