Using Adjoint-Based Forecast Sensitivity to Observation to Evaluate a Wind Profiler Data Assimilation Strategy and the Impact of Data on Short-Term Forecasts
A wind profiler radar detects fine spatiotemporal resolution dynamical information, enabling the capture of meso- and micro-scale systems. Experience gained from observing system experiments (OSEs) studies confirms that reasonable profiler assimilation techniques can achieve improved short-term fore...
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MDPI AG
2024-10-01
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| author | Cheng Wang Xiang-Yu Huang Min Chen Yaodeng Chen Jiqin Zhong Jian Yin |
| author_facet | Cheng Wang Xiang-Yu Huang Min Chen Yaodeng Chen Jiqin Zhong Jian Yin |
| author_sort | Cheng Wang |
| collection | DOAJ |
| description | A wind profiler radar detects fine spatiotemporal resolution dynamical information, enabling the capture of meso- and micro-scale systems. Experience gained from observing system experiments (OSEs) studies confirms that reasonable profiler assimilation techniques can achieve improved short-term forecasts. This study further applies the adjoint-based forecast sensitivity to observation (FSO) method to investigate the quantitative impact of a profiler data assimilation strategy on short-term forecasts, and the results are consistent with those obtained from OSEs, further demonstrating that FSO and OSEs can be used to evaluate the effect of data assimilation techniques from different perspectives. Considering the unique advantage that the FSO can quantify the interactions between various observing systems and the impact on improving the model forecasts according to specific needs without costly additional calculations, we further diagnose in detail the observation impacts from multiple perspectives, including the observation platform, observation variables, and spatial distribution. And the results show that dynamical variables are more significant in improving forecasts compared to the other observed variables. Meanwhile, the dense profiler observations resulted in a more significant impact when radiosonde observations were not detected. The upper-level single winds monitored by profiler radars play a more important role in improving forecast skill. The FSO method measures the impact of an individual observing system, which can be used to enrich the evaluation of data assimilation schemes, efficiently calculate the impacts of multisource observations, and contribute to future development in adaptive observation, observation quality control, and observation error optimization. |
| format | Article |
| id | doaj-art-86641e5be229471192d7da5ee62a3ee1 |
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| issn | 2072-4292 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj-art-86641e5be229471192d7da5ee62a3ee12025-08-20T02:14:23ZengMDPI AGRemote Sensing2072-42922024-10-011621396410.3390/rs16213964Using Adjoint-Based Forecast Sensitivity to Observation to Evaluate a Wind Profiler Data Assimilation Strategy and the Impact of Data on Short-Term ForecastsCheng Wang0Xiang-Yu Huang1Min Chen2Yaodeng Chen3Jiqin Zhong4Jian Yin5Institute of Urban Meteorology, China Meteorological Administration, Beijing 100089, ChinaInstitute of Urban Meteorology, China Meteorological Administration, Beijing 100089, ChinaInstitute of Urban Meteorology, China Meteorological Administration, Beijing 100089, ChinaKey Laboratory of Meteorological Disaster of Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, ChinaInstitute of Urban Meteorology, China Meteorological Administration, Beijing 100089, ChinaInstitute of Urban Meteorology, China Meteorological Administration, Beijing 100089, ChinaA wind profiler radar detects fine spatiotemporal resolution dynamical information, enabling the capture of meso- and micro-scale systems. Experience gained from observing system experiments (OSEs) studies confirms that reasonable profiler assimilation techniques can achieve improved short-term forecasts. This study further applies the adjoint-based forecast sensitivity to observation (FSO) method to investigate the quantitative impact of a profiler data assimilation strategy on short-term forecasts, and the results are consistent with those obtained from OSEs, further demonstrating that FSO and OSEs can be used to evaluate the effect of data assimilation techniques from different perspectives. Considering the unique advantage that the FSO can quantify the interactions between various observing systems and the impact on improving the model forecasts according to specific needs without costly additional calculations, we further diagnose in detail the observation impacts from multiple perspectives, including the observation platform, observation variables, and spatial distribution. And the results show that dynamical variables are more significant in improving forecasts compared to the other observed variables. Meanwhile, the dense profiler observations resulted in a more significant impact when radiosonde observations were not detected. The upper-level single winds monitored by profiler radars play a more important role in improving forecast skill. The FSO method measures the impact of an individual observing system, which can be used to enrich the evaluation of data assimilation schemes, efficiently calculate the impacts of multisource observations, and contribute to future development in adaptive observation, observation quality control, and observation error optimization.https://www.mdpi.com/2072-4292/16/21/3964adjoint-based forecast sensitivity to observation (FSO)data assimilation strategyobservation impact |
| spellingShingle | Cheng Wang Xiang-Yu Huang Min Chen Yaodeng Chen Jiqin Zhong Jian Yin Using Adjoint-Based Forecast Sensitivity to Observation to Evaluate a Wind Profiler Data Assimilation Strategy and the Impact of Data on Short-Term Forecasts Remote Sensing adjoint-based forecast sensitivity to observation (FSO) data assimilation strategy observation impact |
| title | Using Adjoint-Based Forecast Sensitivity to Observation to Evaluate a Wind Profiler Data Assimilation Strategy and the Impact of Data on Short-Term Forecasts |
| title_full | Using Adjoint-Based Forecast Sensitivity to Observation to Evaluate a Wind Profiler Data Assimilation Strategy and the Impact of Data on Short-Term Forecasts |
| title_fullStr | Using Adjoint-Based Forecast Sensitivity to Observation to Evaluate a Wind Profiler Data Assimilation Strategy and the Impact of Data on Short-Term Forecasts |
| title_full_unstemmed | Using Adjoint-Based Forecast Sensitivity to Observation to Evaluate a Wind Profiler Data Assimilation Strategy and the Impact of Data on Short-Term Forecasts |
| title_short | Using Adjoint-Based Forecast Sensitivity to Observation to Evaluate a Wind Profiler Data Assimilation Strategy and the Impact of Data on Short-Term Forecasts |
| title_sort | using adjoint based forecast sensitivity to observation to evaluate a wind profiler data assimilation strategy and the impact of data on short term forecasts |
| topic | adjoint-based forecast sensitivity to observation (FSO) data assimilation strategy observation impact |
| url | https://www.mdpi.com/2072-4292/16/21/3964 |
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