scholarly journals Effects of Assimilating Clear-Sky FY-3D MWHS2 Radiance on the Numerical Simulation of Tropical Storm Ampil

2021 ◽  
Vol 13 (15) ◽  
pp. 2873
Author(s):  
Dongmei Xu ◽  
Aiqing Shu ◽  
Hong Li ◽  
Feifei Shen ◽  
Qiang Li ◽  
...  
Keyword(s):  
Bias Correction ◽  
Skill Score ◽  
Tropical Storm ◽  
Specific Humidity ◽  
Height Increment ◽  
Rain Belt ◽  
Clear Sky ◽  
Microwave Humidity Sounder ◽  
Sky Conditions ◽  
Radiance Data

A new advanced microwave humidity sounder FY-3D MWHS2 radiance has been assimilated under the clear-sky conditions by implementing its data assimilation interface. The case of the tropical storm Ampil in 2018 is selected to address the effectiveness of the new-built module in the initialization and forecast of typhoons. Apart from the experiment assimilating both the Global Telecommunications System (GTS) data and the FY-3D MWHS2 radiance data, an experiment with only GTS data is also conducted for comparison. The results show that the bias correction of this humidity sounder is effective, and the analysis field after assimilating its radiance data matches well with the observation. The increment of specific humidity below the middle layers is evident after the assimilation of the radiance data. Besides, the geopotential height increment and the specific humidity increment at 500 hPa and 850 hPa, respectively, are favorable, resulting in more accurate rain belt distribution and a higher fraction skill score (FSS). In the deterministic forecast, the track error of the FY-3D MWHS2 experiment is consistently less than 90 km.

scholarly journals Combining ground-based microwave radiometer and the AROME convective scale model through 1DVAR retrievals in complex terrain: an Alpine Valley case study

2017 ◽  
Author(s):  
Pauline Martinet ◽  
Domenico Cimini ◽  
Francesco De Angelis ◽  
Guylaine Canut ◽  
Vinciane Unger ◽  
...  
Keyword(s):  
Bias Correction ◽  
Complex Terrain ◽  
Microwave Radiometer ◽  
Scale Model ◽  
Field Campaign ◽  
Alpine Valley ◽  
Clear Sky ◽  
Convective Scale ◽  
Temperature Inversions ◽  
Sky Conditions

Abstract. A RPG-HATPRO ground-based microwave radiometer (MWR) was operated in a deep Alpine valley during the Passy-2015 field campaign. This experiment aims at investigating how stable boundary layers during wintertime conditions drive the accumulation of pollutants. In order to understand the atmospheric processes in the valley, MWR continuously provide vertical profiles of temperature and humidity at a high time frequency, providing valuable information to follow the evolution of the boundary layer. A one-dimensional variational (1DVAR) retrieval technique has been implemented during the field campaign to optimally combine MWR and 1 h forecasts from the French convective scale model AROME. Retrievals were compared to radiosonde data launched at least every 3 hours during two intensive observation periods (IOP). An analysis of the AROME forecast errors during the IOPs has shown a large underestimation of the surface cooling during the strongest stable episode. MWR brightness temperatures were monitored against simulations from the radiative transfer model ARTS2 (Atmospheric Radiative Transfer Simulator) and radiosonde launched during the field campaign. Large errorswere observed for most transparent channels (i.e., 51–52 GHz) affected by absorption model and calibration uncertainties while a good agreement was found for opaque channels (i.e., 54–58 GHz). Based on this monitoring, a bias correction of raw brightness temperature measurements was applied before the 1DVAR retrievals. 1DVAR retrievals were found to significantly improve the AROME forecasts up to 3 km but mainly below 1 km and to outperform usual statistical regressions above 1 km. With the present implementation, a root-mean-square-error (RMSE) of 1 K through all the atmospheric profile was obtained with values within 0.5 K below 500 m in clear-sky conditions. The use of lower elevation angles (up to 5°) in the MWR scanning and the bias correction were found to improve the retrievals below 1000 m. MWR retrievals were found to catch very well deep nearsurface temperature inversions. Larger errors were observed in cloudy conditions due to difficulty of ground-based MWR to resolve high level inversions that are still challenging. Finally, 1DVAR retrievals were optimized for the analysis of the IOPs by using radiosondes as backgrounds in the 1DVAR algorithm instead of the AROME forecasts. A significant improvement of the retrievals in cloudy conditions and below 1000 m in clear-sky was observed. From this study, we can conclude that MWR are expected to bring valuable information into NWP models up to 3 km altitude both in clear-sky and cloudy-sky conditions with the maximum improvement found around 500 m. With an accuracy between 0.5 and 1 K in RMSE, our study has also proved MWR to be capable of resolving deep near-surface temperature inversions observed in complex terrain during highly stable boundary layer conditions.

scholarly journals Comparison of Assimilating All-Sky and Clear-Sky Satellite Radiance for Typhoon Chan-Hom and Nangka Forecasts

Atmosphere ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 599 ◽  
Author(s):  
Jingnan Wang ◽  
Lifeng Zhang ◽  
Jiping Guan ◽  
Mingyang Zhang
Keyword(s):  
Reanalysis Data ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Track Forecast ◽  
Forecast Errors ◽  
Clear Sky ◽  
Warm Core ◽  
Sky Radiance ◽  
Sky Conditions ◽  
Radiance Data

The impacts of assimilating all-sky satellite radiance from the Advanced Microwave Scanning Radiometer 2 (AMSR2) on typhoon Chan-hom and Nangka are evaluated over traditional clear-sky radiance assimilation. Results show that more AMSR2 radiance data around typhoon core area are assimilated in all-sky experiment than clear-sky, which improves the utilization of satellite radiance data. Community Radiative Transfer Model (CRTM) brightness temperature simulation under all-sky conditions is in better agreement with observations than in the case of clear-sky conditions. In a cycle assimilation experiment, all-sky assimilation reduces typhoon track forecast errors by 14.84%, and intensity errors by approximately 16.89%. Wind, temperature and humidity analysis are clearly improved in all-sky assimilation, as evaluated using the European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis data. All-sky assimilation better captures the structures of typhoons, with a stronger warm core and tighter circulation around the typhoon eye. This study explores the contributions to the improvements in all-sky assimilation. These improvements are attributed to the enhancements in initial geopotential height, temperature and moisture in the typhoon core areas. Moreover, assimilating cloud- and precipitation-affected radiance data improves hydrometer simulations, which leads to higher hydrometeor concentrations than clear-sky radiance and conventional data assimilation. The results demonstrate that assimilation of all-sky AMSR2 data improves the analysis and forecast of multiple typhoons.

scholarly journals The Impact of Assimilation of GPM Microwave Imager Clear-Sky Radiance on Numerical Simulations of Hurricanes Joaquin (2015) and Matthew (2016) with the HWRF Model

2019 ◽  
Vol 147 (1) ◽  
pp. 175-198 ◽  
Author(s):  
Zhaoxia Pu ◽  
Chaulam Yu ◽  
Vijay Tallapragada ◽  
Jianjun Jin ◽  
Will McCarty
Keyword(s):  
Data Assimilation ◽  
Bias Correction ◽  
Specific Humidity ◽  
Transfer Model ◽  
Forecast Errors ◽  
Clear Sky ◽  
Sky Radiance ◽  
Microwave Imager ◽  
The Impact ◽  
Hwrf Model

The impact of assimilating Global Precipitation Measurement (GPM) Microwave Imager (GMI) clear-sky radiance on the track and intensity forecasts of two Atlantic hurricanes during the 2015 and 2016 hurricane seasons is assessed using the Hurricane Weather Research and Forecasting (HWRF) Model. The GMI clear-sky brightness temperature is assimilated using a Gridpoint Statistical Interpolation (GSI)-based hybrid ensemble–variational data assimilation system, which utilizes the Community Radiative Transfer Model (CRTM) as a forward operator for satellite sensors. A two-step bias correction approach, which combines a linear regression procedure and variational bias correction, is used to remove most of the systematic biases prior to data assimilation. Forecast results show that assimilating GMI clear-sky radiance has positive impacts on both track and intensity forecasts, with the extent depending on the phase of hurricane evolution. Forecast verifications against dropsonde soundings and reanalysis data show that assimilating GMI clear-sky radiance, when it does not overlap with overpasses of other microwave sounders, can improve forecasts of both thermodynamic (e.g., temperature and specific humidity) and dynamic variables (geopotential height and wind field), which in turn lead to better track forecasts and a more realistic hurricane inner-core structure. Even when other microwave sounders are present (e.g., AMSU-A, ATMS, MHS, etc.), the assimilation of GMI still reduces temperature forecast errors in the near-hurricane environment, which has a significant impact on the intensity forecast.

scholarly journals Evaluation of Assimilating FY-3C MWHS-2 Radiances Using the GSI Global Analysis System

2020 ◽  
Vol 12 (16) ◽  
pp. 2511
Author(s):  
Lipeng Jiang ◽  
Chunxiang Shi ◽  
Tao Zhang ◽  
Yang Guo ◽  
Shuang Yao
Keyword(s):  
Global Analysis ◽  
Advanced Technology ◽  
Specific Humidity ◽  
Height Anomaly ◽  
Similar Data ◽  
Correction Scheme ◽  
Microwave Humidity Sounder ◽  
Analysis System ◽  
One Year ◽  
Radiance Data

The MicroWave Humidity Sounder 2 (MWHS-2) onboard the FY-3C satellite provides an extra important data source for atmospheric water vapor monitoring besides the Microwave Humidity Sounder (MHS) and the Advanced Technology Microwave Sounder (ATMS). This paper introduces MWHS-2 radiance data into the community Gridpoint Statistical Interpolation (GSI) global analysis system. More than one-year cycling assimilation experiments with and without MWHS-2 data are performed. Results show that MWHS-2 has similar data quality to MHS and ATMS. The biases of MWHS-2 are stable except some sudden jumps that can be removed nicely by the variational bias correction scheme within GSI. Assimilating MWHS-2 makes the 6-h forecasts fit more closely to radiosonde observations, with a reduction of 0.55–1% for the observation-minus-simulation standard deviation of specific humidity. The 500 hPa geopotential height anomaly correlation scores are increased by around 0.006 for the 144-h forecast, indicating that assimilating MWHS-2 may also help to improve 3–8-day forecasts.

Evaluation of net shortwave radiation over China with a regional climate model

2020 ◽  
Vol 80 (2) ◽  
pp. 147-163
Author(s):  
X Liu ◽  
Y Kang ◽  
Q Liu ◽  
Z Guo ◽  
Y Chen ◽  
...  
Keyword(s):  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Radiant Energy ◽  
Energy System ◽  
Radiation Budget ◽  
Shortwave Radiation ◽  
Monsoon Region ◽  
Clear Sky ◽  
Sky Conditions

The regional climate model RegCM version 4.6, developed by the European Centre for Medium-Range Weather Forecasts Reanalysis, was used to simulate the radiation budget over China. Clouds and the Earth’s Radiant Energy System (CERES) satellite data were utilized to evaluate the simulation results based on 4 radiative components: net shortwave (NSW) radiation at the surface of the earth and top of the atmosphere (TOA) under all-sky and clear-sky conditions. The performance of the model for low-value areas of NSW was superior to that for high-value areas. NSW at the surface and TOA under all-sky conditions was significantly underestimated; the spatial distribution of the bias was negative in the north and positive in the south, bounded by 25°N for the annual and seasonal averaged difference maps. Compared with the all-sky condition, the simulation effect under clear-sky conditions was significantly better, which indicates that the cloud fraction is the key factor affecting the accuracy of the simulation. In particular, the bias of the TOA NSW under the clear-sky condition was <±10 W m-2 in the eastern areas. The performance of the model was better over the eastern monsoon region in winter and autumn for surface NSW under clear-sky conditions, which may be related to different levels of air pollution during each season. Among the 3 areas, the regional average biases overall were largest (negative) over the Qinghai-Tibet alpine region and smallest over the eastern monsoon region.

Efficient downscaling of satellite oceanographic data with convolutional neural networks

2021 ◽  
Vol 12 (3) ◽  
pp. 46-47
Author(s):  
Nikita Saxena
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Super Resolution ◽  
Sea Surface ◽  
Clear Sky ◽  
Oceanographic Data ◽  
Computationally Expensive ◽  
Sky Conditions ◽  
Data Inference

Space-borne satellite radiometers measure Sea Surface Temperature (SST), which is pivotal to studies of air-sea interactions and ocean features. Under clear sky conditions, high resolution measurements are obtainable. But under cloudy conditions, data analysis is constrained to the available low resolution measurements. We assess the efficiency of Deep Learning (DL) architectures, particularly Convolutional Neural Networks (CNN) to downscale oceanographic data from low spatial resolution (SR) to high SR. With a focus on SST Fields of Bay of Bengal, this study proves that Very Deep Super Resolution CNN can successfully reconstruct SST observations from 15 km SR to 5km SR, and 5km SR to 1km SR. This outcome calls attention to the significance of DL models explicitly trained for the reconstruction of high SR SST fields by using low SR data. Inference on DL models can act as a substitute to the existing computationally expensive downscaling technique: Dynamical Downsampling. The complete code is available on this Github Repository.

Intra-Day DNI Forecasting Under Clear Sky Conditions Using ANFIS

2014 ◽  
Vol 47 (3) ◽  
pp. 10361-10366 ◽  
Author(s):  
Rémi Chauvin ◽  
Julien Nou ◽  
Stéphane Thil ◽  
Stéphane Grieu
Keyword(s):  
Clear Sky ◽  
Sky Conditions

scholarly journals A Blended Sea Ice Concentration Product from AMSR2 and VIIRS

2021 ◽  
Vol 13 (15) ◽  
pp. 2982
Author(s):  
Richard Dworak ◽  
Yinghui Liu ◽  
Jeffrey Key ◽  
Walter N. Meier
Keyword(s):  
Sea Ice ◽  
Spatial Resolution ◽  
Infrared Imaging ◽  
High Spatial Resolution ◽  
Sea Ice Concentration ◽  
Clear Sky ◽  
Ice Concentration ◽  
Best Linear Unbiased ◽  
The Individual ◽  
Sky Conditions

An effective blended Sea-Ice Concentration (SIC) product has been developed that utilizes ice concentrations from passive microwave and visible/infrared satellite instruments, specifically the Advanced Microwave Scanning Radiometer-2 (AMSR2) and the Visible Infrared Imaging Radiometer Suite (VIIRS). The blending takes advantage of the all-sky capability of the AMSR2 sensor and the high spatial resolution of VIIRS, though it utilizes only the clear sky characteristics of VIIRS. After both VIIRS and AMSR2 images are remapped to a 1 km EASE-Grid version 2, a Best Linear Unbiased Estimator (BLUE) method is used to combine the AMSR2 and VIIRS SIC for a blended product at 1 km resolution under clear-sky conditions. Under cloudy-sky conditions the AMSR2 SIC with bias correction is used. For validation, high spatial resolution Landsat data are collocated with VIIRS and AMSR2 from 1 February 2017 to 31 October 2019. Bias, standard deviation, and root mean squared errors are calculated for the SICs of VIIRS, AMSR2, and the blended field. The blended SIC outperforms the individual VIIRS and AMSR2 SICs. The higher spatial resolution VIIRS data provide beneficial information to improve upon AMSR2 SIC under clear-sky conditions, especially during the summer melt season, as the AMSR2 SIC has a consistent negative bias near and above the melting point.

scholarly journals Seasonal Net Shortwave Radiation of Bare Arable Land in Poland and Israel According to Roughness and Atmospheric Irradiance

2021 ◽  
Vol 13 (10) ◽  
pp. 1897
Author(s):  
Jerzy Cierniewski ◽  
Jean-Louis Roujean ◽  
Jarosław Jasiewicz ◽  
Sławomir Królewicz
Keyword(s):  
Arable Land ◽  
Soil Tillage ◽  
Shortwave Radiation ◽  
Landsat 8 ◽  
Clear Sky ◽  
Spatial Coverage ◽  
Arable Fields ◽  
Model Equations ◽  
Earth’S Climate ◽  
Sky Conditions

Tillage of arable fields, using for instance a smoothing harrow, may increase the magnitude of albedo of such soil surfaces depending on the location, the sun’s illumination and atmospheric components. As these soil surfaces absorb less shortwave radiation compared to plowed soils, the result is an atmospheric cooling and a positive effect on the Earth’s climate. This paper is the follow-on of a previous study aimed at quantifying the seasonal dynamics of net shortwave radiation reflected by bare air-dried arable land areas located in contrasting environments, i.e. Poland and Israel. Soil tillage includes a plow, a disk harrow, and a smoothing harrow. Previous work concentrated on the estimate of net shortwave radiation under clear-sky theoretical scenarios, whereas the present study deals with a realistic atmosphere throughout the year 2014. This latter is characterized by the observations of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument on board the Meteosat Second Generation (MSG). The variations of the net shortwave radiation for the selected bare arable land areas were assessed in combining observations from Landsat 8 images and digital maps of land use and soil, plus model equations that calculate the diurnal variations of the broadband blue-sky albedo with roughness inclusive. The daily amount of net shortwave radiation for air-dried bare arable land in Poland and Israel for the time their spatial coverage is the largest was found to be about 40–50% and 10% lower, respectively, in cloudy-sky conditions compared to clear-sky conditions.

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