Poleward expansion of the tropical belt derived from upper tropospheric water vapour

You, Qinglong; Min, Jinzhong; Kang, Shichang; Pepin, Nick

doi:10.1002/joc.4125

Cited by 4 publications

(3 citation statements)

References 25 publications

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“…The temperature-sounding channels of the ATMS differed significantly from those of AMUS-A in spatial sampling, FOV, and noise. However, the HIRS-4 is an atmospheric sounding instrument with multispectral data from one visible channel, as well as seven shortwave and twelve longwave infrared channels obtained from a single telescope and a rotating filter wheel containing twenty individual filters, providing the spatial resolution of approximately 10 km at nadir [31][32][33]. It is also a cross-track sensor, scanning every 6.4 s. It has a swath width of 2248 km to the far edge of the outer FOV centered on the subsatellite track.…”

Section: Data Usedmentioning

confidence: 99%

Satellite Radiance Data Assimilation Using the WRF-3DVAR System for Tropical Storm Dianmu (2021) Forecasts

Thodsan

Torsri³

et al. 2022

Atmosphere

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This study investigated the impact of the assimilation of satellite radiance observations in a three-dimensional variational data assimilation system (3DVAR) that could improve the tracking and intensity forecasts of the Tropical Storm Dianmu in 2021, which occurred over parts of southeast mainland Asia. The weather research and forecasting (WRF) model was used to conduct the assimilation experiments of the storm. Four sets of numerical experiments were performed using the WRF. In the first, the control experiment, only conventional data in Binary Universal Form for the Representation of Meteorological Data (PREPBUFR) observations from the National Centers for Environmental Prediction (NCEP) were assimilated. The second experiment (RDA1) was performed with PREPBUFR observations and satellite radiance data from the Advanced Microwave Unit-A (AMSU-A), and the Advanced Technology Microwave Sounder (ATMS). PREPBUFR observations and the High-resolution Infrared Radiation Sounder (HIRS-4) were used in the third experiment (RDA2). The fourth experiment (ALL-OBS) used the assimilation of PREPBUFR observations and all satellite radiance data (AMSU-A, ATMS, and HIRS-4). The community radiative transfer model was used on the forward operator for the satellite radiance assimilation, along with quality control and bias correction procedures, before assimilating the radiance data. To evaluate the impact of the assimilation experiments, a forecast starting on 00 UTC 23 September 2021, was produced for 72 hours. The results showed that the ALL-OBS experiment improved the short-term forecast up to ~24 hours lead time, as compared to the assimilation considering only PREPBUFR observations. When all observations were assimilated into the model, the storm’s landfall position, intensity, and structure were accurately predicted. In the deterministic forecast, the tracking errors of the ALL-OBS experiment was consistently less than 40 km within 24 hours. The case study of Tropical Storm Dianmu exhibited the significant positive impact of all observations in the numerical model, which could improve updates for initial conditions and storm forecasting.

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Section: Data Usedmentioning

confidence: 99%

Satellite Radiance Data Assimilation Using the WRF-3DVAR System for Tropical Storm Dianmu (2021) Forecasts

Thodsan

Torsri³

et al. 2022

Atmosphere

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“…Lakes are a major source of water for domestic and industrial uses and are vital for riparian zones which together support ecosystems and soil fertility. Water bodies have shown large surface area variations due to many factors including climate change and variability, and anthropogenic impacts such as municipal, irrigation and industrial water use (e.g., [1][2][3][4]). Where climatological factors significantly influence water volume, predictability metrics may be formed.…”

Section: Introductionmentioning

confidence: 99%

“…The prime objective of this study is to use remote sensing data to (1) quantify water body surface area over Lake Mead (in the U.S.A) and Lake Chapala (Mexico) and (2) to investigate the impact of climatological variability on water surface area. To this end, the data collected by several is used to generate various time-series data.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Surface Water Areal changes using Remote Sensing Data

Murray¹,

Khaki²

2020

aeer

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Inland water bodies are crucial for supporting human life in various parts of the world. Therefore, it is essential to accurately monitor its spatiotemporal variations for better water management. The main objective of this study is to investigate the application of remote sensing data for quantifying the surface area changes and the impact of climatological variabilities over Lakes Mead and Chapala. Historical time series of monthly surface area dynamics were developed using Landsat 1-8 scenes and the climate variability was analysed using evaporation rate and precipitation. Results show that estimated surface water changes from satellite data agree well with independent data. A significant decline in surface area of about 40% since 2000 was found over the Lake Mead region. The relationship between surface area, precipitation and evaporation indicate that climatological factors have contributed to the lake surface area reduction. Lake Chapala’s surface area, on the other hand, has not fallen significantly despite negative trends in precipitation. It was found that human interactions with the lake are likely the main cause of surface area variations. The information about water surface area variation in this study is valuable for monitoring and characterising the predictability of water availability of the regions.

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The Greater Mekong Subregion (GMS) and tropical expansion: A regional study of convection and precipitation

Chanalert

Buntoung

Núñez

et al. 2022

Advances in Space Research

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Poleward expansion of the tropical belt derived from upper tropospheric water vapour

Cited by 4 publications

References 25 publications

Satellite Radiance Data Assimilation Using the WRF-3DVAR System for Tropical Storm Dianmu (2021) Forecasts

Satellite Radiance Data Assimilation Using the WRF-3DVAR System for Tropical Storm Dianmu (2021) Forecasts

Analysis of Surface Water Areal changes using Remote Sensing Data

The Greater Mekong Subregion (GMS) and tropical expansion: A regional study of convection and precipitation

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