scholarly journals Potential Influences of Neglecting Aerosol Effects on the NCEP GFS Precipitation Forecast

2017 ◽  
Author(s):  
Mengjiao Jiang ◽  
Jinqin Feng ◽  
Ruiyu Sun ◽  
Zhanqing Li ◽  
Bingcheng Wan ◽  
...  
Keyword(s):  
Water Vapor ◽  
Convective Available Potential Energy ◽  
Heavy Rain ◽  
Forecast Model ◽  
Precipitation Forecast ◽  
Liquid Water Path ◽  
Light Rain ◽  
Forecast Bias ◽  
Rainfall Forecast ◽  
Increasing Trend

Abstract. Aerosol-cloud interactions (ACI) have been widely recognized as a factor affecting precipitation. However, they have not been considered in the operational National Centers for Environmental Predictions Global Forecast System model. We evaluated the potential impact of neglecting ACI on the operational rainfall forecast using ground-based and satellite observations, and model reanalysis. The Climate Prediction Center unified gauge-based precipitation analysis and the Modern-Era Retrospective analysis for Research and Applications, Version 2 aerosol reanalysis were used to evaluate the forecast in three countries for the year 2015. The overestimation of light rain (47.84 %) and underestimation of heavier rain (31.83 %, 52.94 %, and 65.74 % for moderate rain, heavy rain, and very heavy rain, respectively) from the model are qualitatively consistent with the potential errors arising from not accounting for ACI, although other factors cannot be totally ruled out. The standard deviation of the forecast bias is significantly correlated with aerosol optical depth in Australia, the U.S., and China. To gain further insight, we chose the province of Fujian in China to pursue a more insightful investigation using a suite of variables from gauge-based observations of precipitation, visibility, water vapor, convective available potential energy (CAPE), and satellite datasets. Similar forecast biases were found: over-forecasted light rain and under-forecasted heavy rain. Long-term analyses reveal an increasing trend of heavy rain in summer, and a decreasing trend of light rain in other seasons, accompanied by a decreasing trend in visibility, no trend in water vapor, and a slight increasing trend in summertime CAPE. More aerosols decreased cloud effective radii for cases where the liquid water path was greater than 100 g m−2. All findings are consistent with the effects of ACI, i.e., where aerosols inhibit the development of shallow liquid clouds and invigorate warm-base mixed-phase clouds (especially in summertime), which in turn affects precipitation. While we cannot establish rigorous causal relations based on the analyses presented in this study, the significant rainfall forecast bias seen in operational weather forecast model simulations warrants consideration in future model improvements.

scholarly journals Potential influences of neglecting aerosol effects on the NCEP GFS precipitation forecast

2017 ◽  
Vol 17 (22) ◽  
pp. 13967-13982 ◽  
Author(s):  
Mengjiao Jiang ◽  
Jinqin Feng ◽  
Zhanqing Li ◽  
Ruiyu Sun ◽  
Yu-Tai Hou ◽  
...  
Keyword(s):  
Water Vapor ◽  
Convective Available Potential Energy ◽  
Heavy Rain ◽  
Forecast Model ◽  
Precipitation Forecast ◽  
Liquid Water Path ◽  
Light Rain ◽  
Forecast Bias ◽  
Rainfall Forecast ◽  
Increasing Trend

Abstract. Aerosol–cloud interactions (ACIs) have been widely recognized as a factor affecting precipitation. However, they have not been considered in the operational National Centers for Environmental Predictions Global Forecast System model. We evaluated the potential impact of neglecting ACI on the operational rainfall forecast using ground-based and satellite observations and model reanalysis. The Climate Prediction Center unified gauge-based precipitation analysis and the Modern-Era Retrospective analysis for Research and Applications Version 2 aerosol reanalysis were used to evaluate the forecast in three countries for the year 2015. The overestimation of light rain (47.84 %) and underestimation of heavier rain (31.83, 52.94, and 65.74 % for moderate rain, heavy rain, and very heavy rain, respectively) from the model are qualitatively consistent with the potential errors arising from not accounting for ACI, although other factors cannot be totally ruled out. The standard deviation of the forecast bias was significantly correlated with aerosol optical depth in Australia, the US, and China. To gain further insight, we chose the province of Fujian in China to pursue a more insightful investigation using a suite of variables from gauge-based observations of precipitation, visibility, water vapor, convective available potential energy (CAPE), and satellite datasets. Similar forecast biases were found: over-forecasted light rain and under-forecasted heavy rain. Long-term analyses revealed an increasing trend in heavy rain in summer and a decreasing trend in light rain in other seasons, accompanied by a decreasing trend in visibility, no trend in water vapor, and a slight increasing trend in summertime CAPE. More aerosols decreased cloud effective radii for cases where the liquid water path was greater than 100 g m−2. All findings are consistent with the effects of ACI, i.e., where aerosols inhibit the development of shallow liquid clouds and invigorate warm-base mixed-phase clouds (especially in summertime), which in turn affects precipitation. While we cannot establish rigorous causal relations based on the analyses presented in this study, the significant rainfall forecast bias seen in operational weather forecast model simulations warrants consideration in future model improvements.

Indirect Assimilation of Radar Reflectivity with WRF 3D-Var and Its Impact on Prediction of Four Summertime Convective Events

2013 ◽  
Vol 52 (4) ◽  
pp. 889-902 ◽  
Author(s):  
Hongli Wang ◽  
Juanzhen Sun ◽  
Shuiyong Fan ◽  
Xiang-Yu Huang
Keyword(s):  
Water Vapor ◽  
Data Assimilation ◽  
Convective Available Potential Energy ◽  
Three Dimensional ◽  
Heavy Rain ◽  
Cloud Water ◽  
Radar Reflectivity ◽  
Precipitation Forecast ◽  
Weather Research And Forecasting ◽  
Convective System

AbstractAn indirect radar reflectivity assimilation scheme has been developed within the Weather Research and Forecasting model three-dimensional data assimilation system (WRF 3D-Var). This scheme, instead of assimilating radar reflectivity directly, assimilates retrieved rainwater and estimated in-cloud water vapor. An analysis is provided to show that the assimilation of the retrieved rainwater avoids the linearization error of the Z–qr (reflectivity–rainwater) equation. A new observation operator is introduced to assimilate the estimated in-cloud water vapor. The performance of the scheme is demonstrated by assimilating reflectivity observations into the Rapid Update Cycle data assimilation and forecast system operating at Beijing Meteorology Bureau. Four heavy-rain-producing convective cases that occurred during summer 2009 in Beijing, China, are studied using the newly developed system. Results show that on average the assimilation of reflectivity significantly improves the short-term precipitation forecast skill up to 7 h. A diagnosis of the analysis fields of one case shows that the assimilation of reflectivity increases humidity, rainwater, and convective available potential energy in the convective region. As a result, the analysis successfully promotes the developments of the convective system and thus improves the subsequent prediction of the location and intensity of precipitation for this case.

Evaluation and Application of Quantitative Precipitation Forecast Products for Mainland China Based on TIGGE Multimodel Data

2021 ◽  
Vol 22 (5) ◽  
pp. 1199-1219
Author(s):  
Zhangkang Shu ◽  
Jianyun Zhang ◽  
Junliang Jin ◽  
Lin Wang ◽  
Guoqing Wang ◽  
...  
Keyword(s):  
High Reliability ◽  
Mainland China ◽  
Heavy Rain ◽  
Precipitation Forecast ◽  
Hydrological Models ◽  
Light Rain ◽  
Jma Model ◽  
Quantitative Precipitation Forecast ◽  
Rain Events ◽  
Precipitation Events

AbstractWe evaluated 24-h control forecast products from The International Grand Global Ensemble center over the 10 first-class water resource regions of Mainland China in 2013–18 from the perspective of precipitation processes (continuous) and precipitation events (discrete). We evaluated the forecasts from the China Meteorological Administration (CMA), the Centro de Previsão de Tempo e Estudos Climáticos (CPTEC), the Canadian Meteorological Centre (CMC), the European Centre for Medium-Range Weather Forecasts (ECMWF), the Japan Meteorological Agency (JMA), the Korea Meteorological Administration (KMA), the United Kingdom Met Office (UKMO), and the National Centers for Environmental Prediction (NCEP). We analyzed the differences among the numerical weather prediction (NWP) models in predicting various types of precipitation events and showed the spatial variations in the quantitative precipitation forecast efficiency of the NWP models over Mainland China. Meanwhile, we also combined four hydrological models to conduct meteo-hydrological runoff forecasting in three typical basins and used the Bayesian model averaging (BMA) method to perform the ensemble forecast of different scenarios. Our results showed that the models generally underestimate and overestimate precipitation in northwestern China and southwestern China, respectively. This tendency became increasingly clear as the lead time rose. Each model has a high reliability for the forecast of no-rain and light rain in the next 10 days, whereas the NWP model only has high reliability on the next day for moderate and heavy rain events. In general, each model showed different capabilities of capturing various precipitation events. For example, the CMA and CMC forecasts had a better prediction performance for heavy rain but greater errors for other events. The CPTEC forecast performed well for long lead times for no-rain and light rain but had poor predictability for moderate and heavy rains. The KMA, UKMO, and NCEP forecasts performed better for no-rain and light rain. However, their forecasting ability was average for moderate and heavy rain. Although the JMA model performed better in terms of errors and accuracy, it seriously underestimated heavy rain events. The extreme rainstorm and flood forecast results of the coupled JMA model should be treated with caution. Overall, the ECMWF had the most robust performance. Discrepancies in the forecasting effects of various models on different precipitation events vary with the lead time and region. When coupled with hydrological models, NWP models not only control the accuracy of runoff prediction directly but also increase the difference among the prediction results of different hydrological models with the increase in NWP error significantly. Among all the single models, ECMWF, JMA, and NCEP have better effects than the other models. Moreover, the ensemble forecast based on BMA is more robust than the single model, which can improve the quality of runoff prediction in terms of accuracy and reliability.

scholarly journals The Development of a Quantitative Precipitation Forecast Correction Technique Based on Machine Learning for Hydrological Applications

Atmosphere ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 111 ◽  
Author(s):  
Chul-Min Ko ◽  
Yeong Yun Jeong ◽  
Young-Mi Lee ◽  
Byung-Sik Kim
Keyword(s):  
Machine Learning ◽  
Heavy Rainfall ◽  
Extreme Rainfall ◽  
Machine Learning Techniques ◽  
Precipitation Forecast ◽  
Machine Learning Technique ◽  
Rainfall Forecast ◽  
Quantitative Precipitation Forecast ◽  
Correction Technique ◽  
Learning Technique

This study aimed to enhance the accuracy of extreme rainfall forecast, using a machine learning technique for forecasting hydrological impact. In this study, machine learning with XGBoost technique was applied for correcting the quantitative precipitation forecast (QPF) provided by the Korea Meteorological Administration (KMA) to develop a hydrological quantitative precipitation forecast (HQPF) for flood inundation modeling. The performance of machine learning techniques for HQPF production was evaluated with a focus on two cases: one for heavy rainfall events in Seoul and the other for heavy rainfall accompanied by Typhoon Kong-rey (1825). This study calculated the well-known statistical metrics to compare the error derived from QPF-based rainfall and HQPF-based rainfall against the observational data from the four sites. For the heavy rainfall case in Seoul, the mean absolute errors (MAE) of the four sites, i.e., Nowon, Jungnang, Dobong, and Gangnam, were 18.6 mm/3 h, 19.4 mm/3 h, 48.7 mm/3 h, and 19.1 mm/3 h for QPF and 13.6 mm/3 h, 14.2 mm/3 h, 33.3 mm/3 h, and 12.0 mm/3 h for HQPF, respectively. These results clearly indicate that the machine learning technique is able to improve the forecasting performance for localized rainfall. In addition, the HQPF-based rainfall shows better performance in capturing the peak rainfall amount and spatial pattern. Therefore, it is considered that the HQPF can be helpful to improve the accuracy of intense rainfall forecast, which is subsequently beneficial for forecasting floods and their hydrological impacts.

scholarly journals Microphysical Characteristics of Rainfall Observed by a 2DVD Disdrometer during Different Seasons in Beijing, China

2021 ◽  
Vol 13 (12) ◽  
pp. 2303
Author(s):  
Li Luo ◽  
Jia Guo ◽  
Haonan Chen ◽  
Meilin Yang ◽  
Mingxuan Chen ◽  
...  
Keyword(s):  
Rain Rate ◽  
Heavy Rain ◽  
Occurrence Frequency ◽  
Total Rainfall ◽  
Axis Ratio ◽  
Seasonal Differences ◽  
Mean Values ◽  
Light Rain ◽  
Beijing China ◽  
Maximum Occurrence

The seasonal variations of raindrop size distribution (DSD) and rainfall are investigated using three-year (2016–2018) observations from a two-dimensional video disdrometer (2DVD) located at a suburban station (40.13°N, 116.62°E, ~30 m AMSL) in Beijing, China. The annual distribution of rainfall presents a unimodal distribution with a peak in summer with total rainfall of 966.6 mm, followed by fall. Rain rate (R), mass-weighted mean diameter (Dm), and raindrop concentration (Nt) are stratified into six regimes to study their seasonal variation and relative rainfall contribution to the total seasonal rainfall. Heavy drizzle/light rain (R2: 0.2~2.5 mm h−1) has the maximum occurrence frequency throughout the year, while the total rainfall in summer is primarily from heavy rain (R4: 10~50 mm h−1). The rainfall for all seasons is contributed primarily from small raindrops (Dm2: 1.0~2.0 mm). The distribution of occurrence frequency of Nt and the relative rainfall contribution exhibit similar behavior during four seasons with Nt of 10~1000 m−3 registering the maximum occurrence and rainfall contributions. Rainfall in Beijing is dominated by stratiform rain (SR) throughout the year. There is no convective rainfall (CR) in winter, i.e., it occurs most often during summer. DSD of SR has minor seasonal differences, but varies significantly in CR. The mean values of log10Nw (Nw: mm−1m−3, the generalized intercept parameter) and Dm of CR indicate that the CR during spring and fall in Beijing is neither continental nor maritime, at the same time, the CR in summer is close to the maritime-like cluster. The radar reflectivity (Z) and rain rate (?) relationship (Z = ?R?) showed seasonal differences, but were close to the standard NEXRAD Z-R relationship in summer. The shape of raindrops observed from 2DVD was more spherical than the shape obtained from previous experiments, and the effect of different axis ratio relations on polarimetric radar measurements was investigated through T-matrix-based scattering simulations.

METEOROLOGICAL GNSS APPLICATION FOR HEAVY RAIN ON DECEMBER 31, 2019 IN THE JAKARTA AND SURROUNDING AREAS

2021 ◽  
pp. 273
Author(s):  
Syachrul Arief ◽  
Ihsan Muhamad Muafiry
Keyword(s):  
Water Vapor ◽  
Data Processing ◽  
Average Distance ◽  
Heavy Rain ◽  
Research Area ◽  
High Rainfall ◽  
Interesting Pattern ◽  
Surrounding Areas ◽  
Gnss Data ◽  
Second Peak

This study aims to utilize GNSS for meteorology in Indonesia. With the "goGPS" software, the zenith troposphere delay (ZTD) value is estimated. Calculations in rainy conditions, the ZTD value is converted into a water vapor value (PWV). The research area for the phenomenon of heavy rain occurred at the end of 2019 in Jakarta and its surroundings, which caused flooding on January 1, 2020. According to the Geophysical Meteorology and Climatology Agency (BMKG), the flood's primary cause was high rainfall. Meanwhile, the rainfall at Taman Mini and Jatiasih stations was 335 mm/day and 260 mm/day, respectively. We get an interesting pattern of PWV values for this rain phenomenon. GNSS data processing, the PWV value at five GNSS stations around Jakarta, shows the same pattern even though the average distance between GNSS stations is ~ 30 km. The PWV value appears to increase at noon on December 30, 2019, and the peak occurs in the early hours of December 31, 2019. The PWV value suddenly decreases at noon on January 1, 2020. Next, the PWV value increases again but not as high as at the previous peak. Since January 2, 2020, the PWV value has decreased and remained almost constant until January 4, 2020. In that period, there were two events that the PWV value increased. The PWV value at the first peak is ~ 70 mm, and at the second peak ~ 65 mm. The most significant increase in PWV value was recorded at CJKT stations.

scholarly journals A Study of the Distribution and Variability of Cloud Water Using ISCCP, SSM/I Cloud Product, and Reanalysis Datasets

2014 ◽  
Vol 27 (9) ◽  
pp. 3114-3128 ◽  
Author(s):  
Zhiwei Heng ◽  
Yunfei Fu ◽  
Guosheng Liu ◽  
Renjun Zhou ◽  
Yu Wang ◽  
...  
Keyword(s):  
Cloud Water ◽  
Positive Anomaly ◽  
Liquid Water Path ◽  
Water Path ◽  
The North ◽  
Increasing Trend ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Microwave Imager ◽  
Ice Water

Abstract In this paper, the global distribution of cloud water based on International Satellite Cloud Climatology Project (ISCCP), Moderate Resolution Imaging Spectroradiometer (MODIS), CloudSat Cloud Profiling Radar (CPR), European Center for Medium-Range Weather Forecasts Interim Re-Analysis (ERA-Interim), and Climate Forecast System Reanalysis (CFSR) datasets is presented, and the variability of cloud water from ISCCP, the Special Sensor Microwave Imager (SSM/I), ERA-Interim, and CFSR data over the time period of 1995 through 2009 is discussed. The results show noticeable differences in cloud water over land and over ocean, as well as latitudinal variations. Large values of cloud water are mainly distributed over the North Pacific and Atlantic Oceans, eastern ITCZ, regions off the west coast of the continents as well as tropical rain forest. Cloud water path (CWP), liquid water path (LWP), and ice water path (IWP) from these datasets show a relatively good agreement in distributions and zonal means. The results of trend analyzing show an increasing trend in CWP, and also a significant increasing trend of LWP can be found in the dataset of ISCCP, ERA-Interim, and CFSR over the ocean. Besides the long-term variation trend, rises of cloud water are found when temperature and water vapor exhibit a positive anomaly. EOF analyses are also applied to the anomalies of cloud water, the first dominate mode of CWP and IWP are similar, and a phase change can be found in the LWP time coefficient around 1999 in ISCCP and CFSR and around 2002 in ERA-Interim.

scholarly journals The source of discrepancies in aerosol–cloud–precipitation interactions between GCM and A-Train retrievals

2016 ◽  
Vol 16 (23) ◽  
pp. 15413-15424 ◽  
Author(s):  
Takuro Michibata ◽  
Kentaroh Suzuki ◽  
Yousuke Sato ◽  
Toshihiko Takemura
Keyword(s):  
General Circulation ◽  
Climate Models ◽  
Circulation Model ◽  
Cloud Droplet ◽  
Systematic Difference ◽  
Liquid Water Path ◽  
Light Rain ◽  
Aerosol Cloud ◽  
Nonlinear Complexity ◽  
Aerosol Cloud Interactions

Abstract. Aerosol–cloud interactions are one of the most uncertain processes in climate models due to their nonlinear complexity. A key complexity arises from the possibility that clouds can respond to perturbed aerosols in two opposite ways, as characterized by the traditional “cloud lifetime” hypothesis and more recent “buffered system” hypothesis. Their importance in climate simulations remains poorly understood. Here we investigate the response of the liquid water path (LWP) to aerosol perturbations for warm clouds from the perspective of general circulation model (GCM) and A-Train remote sensing, through process-oriented model evaluations. A systematic difference is found in the LWP response between the model results and observations. The model results indicate a near-global uniform increase of LWP with increasing aerosol loading, while the sign of the response of the LWP from the A-Train varies from region to region. The satellite-observed response of the LWP is closely related to meteorological and/or macrophysical factors, in addition to the microphysics. The model does not reproduce this variability of cloud susceptibility (i.e., sensitivity of LWP to perturbed aerosols) because the parameterization of the autoconversion process assumes only suppression of rain formation in response to increased cloud droplet number, and does not consider macrophysical aspects that serve as a mechanism for the negative responses of the LWP via enhancements of evaporation and precipitation. Model biases are also found in the precipitation microphysics, which suggests that the model generates rainwater readily even when little cloud water is present. This essentially causes projections of unrealistically frequent and light rain, with high cloud susceptibilities to aerosol perturbations.

Topographic amplification of crustal subsidence by the rainwater load of the 2018 heavy rain in SW Japan

2021 ◽  
Author(s):  
Syachrul Arief
Keyword(s):  
Water Vapor ◽  
Precipitable Water ◽  
Heavy Rain ◽  
Satellite System ◽  
Rain Gauge ◽  
Precipitable Water Vapor ◽  
Surface Load ◽  
Rain Area ◽  
Movement Data ◽  
Sw Japan

<p>The huge amount of water vapor in the atmosphere caused disastrous heavy rain and floods in early July 2018 in SW Japan. Here I present a comprehensive space geodetic study of water brought by this heavy rain done by using a dense network of Global Navigation Satellite System (GNSS) receivers. </p><p>First, I reconstruct sea level precipitable water vapor above land region on the heavy rain. The total amount of water vapor derived by spatially integrating precipitable water vapor on land was ~25.8 Gt, which corresponds to the bucket size to carry water from ocean to land. I then compiled the precipitation measured with a rain radar network. The data showed the total precipitation by this heavy rain as ~22.11 Gt.</p><p>Next, I studied the crustal subsidence caused by the rainwater as the surface load. The GNSS stations located under the heavy rain area temporarily subsided 1-2 centimeters and the subsidence mostly recovered in a day. Using such vertical crustal movement data, I estimated the distribution of surface water in SW Japan. </p><p>The total amount of the estimated water load on 6 July 2018 was ~68.2 Gt, which significantly exceeds the cumulative on-land rainfalls of the heavy rain day from radar rain gauge analyzed precipitation data. I consider that such an amplification of subsidence originates from the selective deployment of GNSS stations in the concave places, e.g. along valleys and within basins, in the mountainous Japanese Islands.</p>

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