Automatic Weather Stations Research Articles

1991–2020 climate normal in the European Alps: focus on high-elevation environments

Alps are an important geographical area of the European continent and, in this area, temperature increase is most evident. However, the 1991–2020 climate normal in the Alps has still not been thoroughly investigated. Aiming to fill this gap with a focus on high-elevation environments, minimum and maximum daily air temperature acquired by 23 automatic weather station were used. The results show that the mean annual values of minimum and maximum temperature for the 1991–2020 climate normal in the Alps are −2.4°C and 4.4°C, respectively, with a warming rate of 0.5°C/10 years. The mean annual temperature comparison between 1961–1990 and 1971–2000, 1961–1990 and 1981–2010, 1961–1990 and 1991–2020 climate normal show an increase of 0.3°C, 0.5°C and 0.9°C, respectively. The results also confirm that seasonal and annual temperatures are rising through the whole Alpine arc, mainly in summer and autumn. This work highlights that annual minimum and maximum temperature do not seem to be affected by a positive elevation-dependent warming. Instead, a positive elevation-dependent warming in the maximum values of the annual minimum temperature was found. If anthropogenic emissions maintain the trend of the last decades, the expected mean annual temperature of the 2001–2030 climate normal is −0.2°C, with an increase of 0.5°C if compared to the 1991–2020 climate normal and with an increase of 1.5°C if compared to the 1961–1990 climate normal. This study highlights the warming rate that is now present in the European Alps, provides indications on the warming rate that will occur in the coming years and highlights the importance of carrying out investigations that consider not only the last 30-year climate normal, but also the most recent 30-year climate normal by comparing them with each other.

An Analysis for the Applicability of Global Precipitation Measurement Mission (GPM) IMERG Precipitation Data in Typhoons

This study selected examples of 17 typhoons that landed in Fujian after passing through Taiwan. The study evaluated the precipitation in different time scales and the spatial distribution of daily precipitation of varying magnitudes in the southeastern coastal area by comparing satellite precipitation estimation products with meteorological observation station data. The evaluation used a correlation coefficient, mean relative error, relative bias, and graded assessment indexes (probability of detection, false alarm rate, and critical success index). Correlation coefficient analysis revealed that maximum daily precipitation performed best, followed by process total precipitation. The relative bias indicates that the precipitation estimated by the satellite is lower than the rainfall recorded by the automatic weather station. Mean relative error analysis showed that hourly precipitation had the highest error, followed by maximum daily precipitation. The GPM IMERG precipitation products’ retrieval of daily precipitation of varying magnitudes was assessed using three indicators. The assessment revealed that the satellite had a low under-reporting rate for light rain events but a high under-reporting rate for torrential rain events, especially extremely heavy rainstorm events, in terms of probability of detection. For the false alarm rate, the satellite had a small probability of false predictions for light rain events, while extremely heavy rainstorm events had the highest probability. For the critical success index, the satellite’s estimation of light rain events was basically consistent with reality; however, its ability to estimate precipitations above rainstorm levels was low. The results of the spatial assessment of heavy precipitation show that the satellite’s ability to detect heavy precipitation’s structure, intensity, and location is fair and has some reference value, especially for regions where conventional information is scarce.

Impacts of UHI on Heating and Cooling Loads in Residential Buildings in Cities of Different Sizes in Beijing–Tianjin–Hebei Region in China

The heating and cooling energy consumption levels of urban buildings account for a large and rapidly growing proportion of the total end-use energy consumption of society. The urban heat island (UHI) effect is an important factor influencing the spatiotemporal variations in the heating and cooling energy consumption levels of buildings. However, there is a lack of research on the impact of the UHI on the heating and cooling energy consumption of buildings in cities of different sizes in the Beijing–Tianjin–Hebei urban agglomeration, which is the most urbanized region in northern China. We selected rural reference stations using the remote sensing method, and applied an hourly data set from automatic weather stations, to examine the impact of the UHI on the typical residential building heating and cooling loads in three cities of varied sizes in the Beijing–Tianjin–Hebei urban agglomeration through building energy simulation. The main conclusions were as follows. As the UHI intensity (UHII) increased, the heating load difference between urban and rural areas decreased, while the cooling load difference between urban and rural areas increased in the cities. The average daily heating loads in the urban areas of Beijing, Tianjin, and Shijiazhuang were 8.14, 10.71, and 2.79% lower than those in their rural areas, respectively, while the average daily cooling loads in the urban areas were 6.88, 6.70, and 0.27% higher than those in their rural areas, respectively. Moreover, the absolute hourly load differences between urban and rural areas were significantly larger during the heating periods than during the cooling periods, with the former characterized by being strong at night and weak during the day. During the peak energy load period, the contribution of the UHI to the peak load of residential buildings varied between the cities. During the stable high-load period, from 18:00 to 07:00 the next day in the heating periods (from 18:00 to 05:00 the next day in the cooling periods), the hourly loads in the urban areas of Beijing, Tianjin, and Shijiazhuang were 3.15 (2.48), 3.88 (1.51), and 1.07% (1.09%) lower (higher) than those in their rural areas, respectively. Our analysis highlights the necessity to differentiate the energy supplies for the heating and cooling of urban buildings in different sized cities in the region.

Meltwater runoff and glacier mass balance in the high Arctic: 1991–2022 simulations for Svalbard

Abstract. The Arctic is undergoing increased warming compared to the global mean, which has major implications for freshwater runoff into the oceans from seasonal snow and glaciers. Here, we present high-resolution (2.5 km) simulations of glacier mass balance, runoff, and snow conditions on Svalbard from 1991–2022, one of the fastest warming regions in the world. The simulations are created using the CryoGrid community model forced by Copernicus Arctic Regional ReAnalysis (CARRA) (1991–2021) and AROME-ARCTIC forecasts (2016–2022). Updates to the water percolation and runoff schemes are implemented in the CryoGrid model for the simulations. In situ observations available for Svalbard, including automatic weather station data, stake measurements, and discharge observations, are used to carefully evaluate the quality of the simulations and model forcing. We find a slightly negative climatic mass balance (CMB) over the simulation period of −0.08 mw.e.yr-1 but with no statistically significant trend. The most negative annual CMB is found for Nordenskiöldland (−0.73 mw.e.yr-1), with a significant negative trend of −0.27 mw.e. per decade for the region. Although there is no trend in the annual CMB, we do find a significant increasing trend in the runoff from glaciers of 0.14 mw.e. per decade. The average runoff was found to be 0.8 mw.e.yr-1. We also find a significant negative trend in the refreezing of −0.13 mw.e. per decade. Using AROME-ARCTIC forcing, we find that 2021/22 has the most negative CMB and highest runoff over the 1991–2022 simulation period investigated in this study. We find the simulated climatic mass balance and runoff using CARRA and AROME-ARCTIC forcing are similar and differ by only 0.1 mw.e.yr-1 in climatic mass balance and by 0.2 mw.e.yr-1 in glacier runoff when averaged over all of Svalbard. There is, however, a clear difference over Nordenskiöldland, where AROME-ARCTIC simulates significantly higher mass balance and significantly lower runoff. This indicates that AROME-ARCTIC may provide similar high-quality predictions of the total mass balance of Svalbard as CARRA, but regional uncertainties should be taken into consideration. The simulations produced for this study are made publicly available at a daily and monthly resolution, and these high-resolution simulations may be re-used in a wide range of applications including studies on glacial runoff, ocean currents, and ecosystems.

An empirical model to calculate snow depth from daily snow water equivalent: SWE2HS 1.0

Abstract. Many methods exist to model snow densification in order to calculate the depth of a single snow layer or the depth of the total snow cover from its mass. Most of these densification models need to be tightly integrated with an accumulation and melt model and need many forcing variables at high temporal resolution. However, when trying to model snow depth (HS) on climatological timescales, which is often needed for winter tourism-related applications, these preconditions can cause barriers. Often, for these types of applications, empirical snow models are used. These can estimate snow accumulation and snowmelt based on daily precipitation and temperature data only. To convert the resultant snow water equivalent (SWE) time series into snow depth, we developed the empirical model SWE2HS. SWE2HS is a multilayer densification model which uses daily snow water equivalent as sole input. A constant new snow density is assumed and densification is calculated via exponential settling functions. The maximum snow density of a single layer changes over time due to overburden and SWE losses. SWE2HS has been calibrated on a data set derived from a network of manual snow stations in Switzerland. It has been validated against independent data derived from automatic weather stations (AWSs) in the European Alps (Austria, France, Germany, Switzerland) and against withheld data from the Swiss manual observer station data set which was not used for calibration. The model fits the calibration data with root mean squared error (RMSE) of 8.4 cm, coefficient of determination (R2) of 0.97, and bias of −0.3 cm; it is able to achieve RMSE of 20.5 cm, R2 of 0.92, and bias of 2.5 cm on the validation data set from automatic weather stations and RMSE of 7.9 cm, R2 of 0.97, and bias of −0.3 cm on the validation data set from manual stations. The temporal evolution of the bulk density can be reproduced reasonably well on all three data sets. Due to its simplicity, the model can be used as post-processing tool for output of any other snow model that provides daily snow water equivalent output. Owing to its empirical nature, SWE2HS should only be used in regions with a similar snow climatology as the European Alps or has to be recalibrated for other snow climatological conditions. The SWE2HS model is available as a Python package which can be easily installed and used.

Impact of Climate Change on the Spatio-Temporal Variation in Groundwater Storage in the Guangdong–Hong Kong–Macao Greater Bay Area

The Guangdong–Hong Kong–Macao Greater Bay Area (GBA) is one of the world’s four major bay areas. Groundwater is indispensable in ensuring water supply for human production and living, as well as social and economic development. Studying the spatial–temporal variation in groundwater storage (GWS) and exploring the impact of climate change on GWS is of great significance for water resource management in the GBA. In this work, we conducted a simulation using the Community Land Model version 5.0 (CLM5.0) and combined it with Gravity Recovery and Climate Experiment (GRACE) data to calculate GWS in the GBA. In addition, based on the multiple linear regression model, we quantitatively assessed the effects of different climate factors on the change in GWS in the GBA. Comparisons with groundwater wells, automatic weather stations, and satellite observations demonstrated reasonable results. Our results showed that precipitation and evapotranspiration are the main factors affecting the change in GWS in the GBA. Precipitation dominates GWS anomaly changes in areas where wetting and precipitation vary drastically, such as the northern part of Foshan. GWS is closely related to evapotranspiration, in which water and heat changes are significant.

Surface energy balance on a polythermal glacier, Arctic, and the role of poleward atmospheric moisture transport

In recent years, increasing moisture over the Arctic and subarctic regions shows that the Arctic is experiencing wetting conditions, and poleward atmospheric moisture transport (PAMT) plays a vital role in the atmosphere-ice/snow interactions in the Arctic. To identify the effect of PAMT on the surface energy balance (SEB) for the Arctic glacier, meteorological data from an automatic weather station (AWS) at 377 m a.s.l. on the Austre Lovénbreen glacier, as well as the ERA5 reanalysis data from 30 April 2014 to 30 April 2015, were analyzed in this paper. Our results show that the net shortwave radiation (Snet) (18 W m−2, 55%), turbulent sensible (H) (14 W m−2, 43%) and subsurface heat flux (G) (1 W m−2, 2%) act as the energy sources, while the net longwave radiation (Lnet) (−19 W m−2, 56%), melt energy (Qm) (−12 W m−2, 35%) and turbulent latent heat flux (LE) (−3 W m−2, 9%) represent the heat sink. The development process of Atmospheric River (AR), primary form of PAMT, near the Svalbard in February 2015 was revealed according to the total column water vapor, 850 hPa winds and geopotential height fields. Compared to summer, the glacier SEB was more affected in winter by PAMT, leading to a frequent overcast and thus increasing air temperature, moisture, and wind speed, all of which are variables governing the SEB by decreasing H, LE and G less than increasing the Lnet. Our findings obtained here can help to better understand the diverse SEB change on the high Arctic glacier from the perspective of PAMT and large-scale atmospheric circulations anomalies.

Snow identification from unattended automatic weather stations images using DANet

Snow identification from unattended automatic weather stations images using DANet

Global Horizontal Irradiance in West Africa: Evaluation of the WRF-Solar Model in Convection-Permitting Mode with Ground Measurements

Abstract The number of solar power plants has increased in West Africa in recent years. Reliable reanalysis data and short-term forecasting of solar irradiance from numerical weather prediction models could provide an economic advantage for the planning and operation of solar power plants, especially in data-poor regions such as West Africa. This study presents a detailed assessment of different shortwave (SW) radiation schemes from the Weather Research and Forecasting (WRF) Model option Solar (WRF-Solar), with appropriate configurations for different atmospheric conditions in Ghana and the southern part of Burkina Faso. We applied two 1-way nested domains (D1 = 15 km and D2 = 3 km) to investigate four different SW schemes, namely, the Community Atmosphere Model, Dudhia, RRTMG, Goddard, and RRTMG without aerosol and with aerosol inputs (RRTMG_AERO). The simulation results were validated using hourly measurements from different automatic weather stations established in the study region in recent years. The results show that the RRTMG_AERO_D01 generally outperforms the other SW radiation schemes to simulate global horizontal irradiance under all-sky condition [RMSE = 235 W m−2 (19%); MAE = 172 W m−2 (14%)] and also under cloudy skies. Moreover, RRTMG_AERO_D01 shows the best performance on a seasonal scale. Both the RRTMG_AERO and Dudhia experiments indicate a good performance under clear skies. However, the sensitivity study of different SW radiation schemes in the WRF-Solar model suggests that RRTMG_AERO gives better results. Therefore, it is recommended that it be used for solar irradiance forecasts over Ghana and the southern part of Burkina Faso.

Quantification of urban heat islands using automatic weather station data and smart-city networks

Quantification of urban heat islands using automatic weather station data and smart-city networks

Atmospheric Ducts and Their Electromagnetic Propagation Characteristics in the Northwestern South China Sea

The propagation of electromagnetic waves beyond the line of sight can be caused by atmospheric ducts, which are significant concerns in the fields of radar and communication. This paper utilizes data from seven automatic weather stations and five radio-sounding stations to statistically analyze the characteristics of the atmospheric ducts in the northwest region of the South China Sea (SCS). After verifying the practicality of numerical analysis data from NCEP CFSv2 and ECMWF in studying atmospheric ducts using measured data, we analyzed the spatial–temporal distribution characteristics of the height of the regional evaporation duct and the bottom height of the elevated duct. The study found that the NCEP CFSv2 data accurately capture the evaporation duct height and duct occurrence rate in the study area, and the elevated duct bottom height calculated from ERA5 and the measured data have good consistency. The occurrence rate and height of the evaporation duct in coastal stations in the northwest of the SCS vary significantly by month, demonstrating clear monthly distribution patterns; conversely, changes in the Xisha station are minimal, indicating good temporal uniformity. For lower atmospheric ducts, the difference in occurrence rates between 00:00 and 12:00 (UTC) is negligible. The occurrence probability of elevated ducts in the Beibu Gulf area is relatively high, mainly concentrated from January to April, and the Xisha area is dominated by surface ducts without foundation layers, mainly concentrated from June to August. Monsoons play a critical role in the generation and evolution of atmospheric ducts in the northwest of the SCS, with the height of the evaporation duct increasing and the bottom height of the elevated duct decreasing after the onset of the summer monsoon. In the end, we simulated electromagnetic propagation loss under different frequencies and radiation elevation angles in various duct environments within a typical atmospheric duct structure.

Experimental study of spatial and seasonal temperature characteristics of Jalingo metropolis

The paper presents the results of studying the spatial and seasonal atmospheric temperature characteristics of Jalingo metropolis, Nigeria, with the aim of assessing seasonal temperature variations for thermal comfort planning. Temperature data for this study was collected across the local climate zones, otherwise known as thermal climate zones (TCZ), following the TZC classification of the area. Temperature data was collected for 30 days continuously and simultaneously across the TCZ. The collection of temperature data was done in the months of April and August 2021, and January 2022 – representing the dry season, the rainy season and the Harmattan period respectively. A Thermochron Ibutton sensor by Maxim Incorporation was used to collect temperature data, while the specific series used was DS1921. An improvised automated weather station was constructed to house the sensor. The result of the study indicates that air temperature varies considerably within Jalingo metropolis, as well as throughout the seasons, thus affecting human thermal comfort in the study area, as temperature is found to correlate well with both human morbidity and mortality, especially regarding cardiovascular diseases. The paper recommends increasing ventilation and nature-based solution to reduce high temperatures through tree planting in order to improve human thermal comfort in Jalingo metropolis area.

Interannual Variability in the Coastal Zones of the Gulf of California

Few studies have explored the details of climatology in the Gulf of California (GoC) coastal zone, a region characterized by robust land–sea breeze circulation that results from land heating on both coasts of the GoC. Using hourly historical observations from automatic weather stations (AWSs) from 2008 to 2018, we performed harmonic and empirical orthogonal function analyses to describe the climatology of several characteristics that are regularly monitored in the GoC coastal zone. The characteristics included air temperature (°C), relative humidity (%), atmospheric pressure (hPa), wind intensity (m s−1), and wind direction (°). The National Water Commission (CNA) provided records for stations located along the coast of the GoC. The results revealed an intense annual and, to a lesser extent, interannual signal for all characteristics. The presence of synoptic patterns forces seasonal and intraseasonal variations to occur. In summer, tropical systems increase the seasonal variability, mainly at the eastern mouth of the GoC. Some stations display this increase until the cold season arrives with the passage of winter systems. Finally, we found that interannual variability could be associated with El Niño–Southern Oscillation events.

Height correction method based on the Monin–Obukhov similarity theory for better prediction of near-surface wind fields

We developed the height correction algorithm based on the Monin–Obukhov similarity theory for application in Weather Research and Forecasting (WRF) model outputs to correct the 10-m height definition and match weather station observations. Thirty simulations were conducted for April 2012 to validate the height correction algorithm against observations such as the automated surface observing system and automatic weather stations. The WRF model simulations were constructed with two nested domains with horizontal grid spacings of 10 and 1 km. The root mean square error of the diagnosed 10-m wind speed after applying the height correction algorithm was consistently reduced compared to the one before applying the algorithm over the innermost domain during the analysis period. The impact of the height correction on diagnosed wind speed was greater when the environment was unstable and the magnitude of the roughness length was large. Evaluation showed that a strong synoptic forcing condition results in a significant reduction in the bias of wind speed.

A Climate Change Study: Downscaling of Climatic Parameters and Their Assessment Over East Rathong Glacier of Eastern Himalayan Region

In this study monthly Temperature Lapse Rates and basic downscaling technique of subtracting means are used to construct temperature (T) series over East Rathong glacier by using multiple data sets from Automatic Weather Stations (AWS) at Himalayan Mountaineering Institute (HMI), Gangtok Indian Meteorological Department (IMD), Yuksum and ERA5reanalysis data. Precipitation (P) over glacier is estimated using Gangtok IMD data and downscaled ERA5 data. The P data is downscaled using local scaling factor method. Change in volume of 0.180 km3 (from 1962 to 2020) estimated using satellite data are used to construct Mass Balance (MB) series for the glacier. It is observed that the glacier is losing its mass with increasing T, Aerosol Optical Depth (AOD) (from 0.1 in 1980 to 0.2 in 2020), and increasing Pmainly during June-July-August months when temperatures are already high. This study is important as it used multiple data sets along with field data extracted from the literature to study climatic impacts on East Rathong glacier. Data generated from this study may be used in mass balance projection of the glaciers in the absence of field data.

#2708 THE EFFECT OF PERCEIVED TEMPERATURE ON THE MORTALITY IN CHRONIC KIDNEY DISEASE PATIENTS

Abstract Background and Aims With the global warming, the interest in health risks from the high temperature exposure is growing. The perceived temperature is an equivalent temperature based on a complete heat budget model of the human body. We aimed to analyse the effect of perceived temperature on the overall mortality among patients with chronic kidney disease. Method A total of 32,870 patients with chronic kidney disease living in Seoul metropolitan region were recruited in a retrospective cohort (2001-2018). The perceived temperature during summer season (from July to September, at each year) was calculated using various climate factors including air temperature nearby automated weather station, dew point temperature, wind velocity, height of anemometer above ground, and total cloud amount. We assessed the association of perceived temperature using inverse distance weighting on mortality in patients with chronic kidney disease in the Cox proportional hazard model that was adjusted for sex, age, body mass index, estimated glomerular filtration rate, hypertension, and diabetes mellitus status. Results During the 6.14±3.96 years, 3,863 deaths (13%) were observed. We confirmed the significant effects of perceived temperature (average perceived temperature: hazard ratio [HR] 1.21, 95% confidence interval [CI] 1.18-1.23; minimum perceived temperature: HR 1.02, 95% CI 1.00-1.05; maximum perceived temperature: 1.20, 95% CI 1.18-1.22) on mortality among patients with chronic kidney disease in univariable analysis. In multivariable analysis, average perceived temperature (HR 1.22, 95% CI 1.19-1.25) and maximum perceived temperature (HR 1.20, 95% CI 1.17-1.23) showed increased risk for overall mortality among patients with chronic kidney disease. Conclusion Long-term exposure to high perceived temperature during summer season increased the risk of mortality among patients with chronic kidney disease.

Deriving crop coefficients for evergreen and deciduous fruit orchards in South Africa using the fraction of vegetation cover and tree height data

Inaccurate crop coefficients are major contributing sources of uncertainty that lead to inefficient use of limited available water resources. Understanding the need to improve water use efficiency in South Africa’s fruit industry, this study evaluated the method of deriving crop coefficients developed by Allen and Pereira (2009) over a variety of irrigated fruit tree crops. Detailed data of transpiration, evapotranspiration and weather variables measured using the heat ratio method, eddy covariance method and automatic weather stations, were collected from a water research funding body established by the South African government. This study adjusted the stomatal sensitivity function (Fr) in the model by replacing the ratio of the leaf resistance (rl) to the standard leaf resistance of a reference crop (100 sm−1) with rl/α where α is a resistance parameter for the specific crop. The resistance parameter was solved accordingly for each fruit type. Respective unique α values were obtained as: macadamia nuts (200 sm−1), citrus (50 s m−1), peaches (20 s m−1) and pecans (20 s m−1). These unique values were used to simulate basal and single crop coefficients that produced satisfactory results when compared to the actual measured values. Overly, no unique standard α value exists for most tree crops although a value close to 20 sm−1 may give reasonable estimates for pome and stone fruit. Crop coefficients derived using locally measured data were standardised and tabulated in a format that facilitates their transferability between sites. However, there is still a need to acquire crop specific information to parameterize α and improve accuracies.

Insights on Satellite-Based IMERG Precipitation Estimates at Multiple Space and Time Scales for a Developing Urban Region in India

Abstract Satellite-based rainfall estimates are a great resource for data-scarce regions, including urban regions, because of its finer resolution. Integrated Multi-satellitE Retrievals for GPM (IMERG) is a widely used product and is evaluated at a city scale for the Hyderabad region using two different ground truths, i.e., India Meteorological Department (IMD) gridded rainfall and Telangana State Development Planning Society (TSDPS) automatic weather station (AWS) measured rainfall. The IMERG rainfall estimates are evaluated on multiple spatial and temporal scales as well as on a rainfall event scale. Both continuous and categorical verification metrics suggest good performance of IMERG on the daily scale; however, relatively decreased performance was observed on the hourly scale. Underestimated and overestimated IMERG estimates with respect to IMD gridded rainfall and AWS measured rainfall, respectively, suggest the performance depends on type of ground truth. Unlike categorical metrics, RMSE and PBIAS have a pattern implying a systematic error with respect to rainfall amount. Further, sample size, diurnal variations, and season are found to have a role in IMERG estimates’ performance. Temporal aggregation of hourly to daily time scales showed the improved IMERG performance; however, no spatial-scale dependence was observed among zonewise and Hyderabad region–wise rainfall estimates. Comparison of raw and bias-corrected IMERG rainfall-based intensity–duration–frequency (IDF) curves with corresponding hourly rain gauge IDF curves showcases the value addition via simple bias correction techniques. Overall, the study suggests the IMERG estimates can be used as an alternative data source, and it can be further improved by modifying the retrieval algorithm. Significance Statement Many urban regions are typically data sparse, which limits scientific understanding and reliable engineering designs of various urban hydrometeorology-relevant tasks, including climatological and extreme rainfall characterization, flood hazard assessment, and stormwater management systems. Satellite rainfall estimates come as a great resource and Integrated Multi-satellitE Retrievals for GPM (IMERG) acts as a best alternative. The Hyderabad region, the sixth-largest metropolitan area in India, is selected to analyze the widely used satellite estimates, i.e., retrievals for GPM. The study observed inaccuracies in the IMERG estimates that varied with rainfall magnitudes and space and time scales; nonetheless, the estimates can be used as an alternative data source for decision-making such as whether rain exceeds a certain threshold or not.

The extraordinarily large vortex structure of Typhoon In-fa (2021), observed by spaceborne microwave radiometer and synthetic aperture radar

Tropical cyclone (TC) vortex structure can be estimated from wind radii (34, 50, and 64 kt). Accurate wind radii are essential for assessing the impact range of TCs. Combining observations from active and passive microwave remote sensing instruments can provide long-time series data for monitoring changes in TC wind structure. Here, the evolution of the wind radii for Typhoon In-fa (2021), from its genesis to its first landfall, is evaluated using data from spaceborne microwave radiometers and synthetic aperture radars (SARs). Our results show that the retrieved wind radii are relatively close to those from the Joint Typhoon Warning Center (JTWC), the coastal automatic weather stations in China, and the advanced scatterometer. The time series for the wind radii show significant increasing trends during the lifecycle of In-fa and a massive vortex structure before In-fa made landfall. The average wind radii observed before landfall are all likely in the 90th percentile of the 2001–2021 JTWC historical wind radii records, indicating that the vortex structure is extraordinarily large before In-fa made landfall and represents a historical extreme. The extremely large vortex structure may be attributable to the interaction between In-fa and an extremely strong monsoon gyre.

Snowmelt detection on the Antarctic ice sheet and ice Shelves based on AMSR2 89 GHz channels

Currently, the spatial resolution of snowmelt detection based on low-frequency brightness temperature data (less than or equal to 36 GHz) from the Advanced Microwave Scanning Radiometer 2 (AMSR2) is low, which inhibits the determination of accurate snowmelt changes. The spatial resolution of the AMSR2 89 GHz data is at least twice that of other microwave channels, but liquid water in clouds seriously affects the propagation of the high-frequency microwave signal. In order to apply microwave radiometer AMSR-2 89 GHz data with high spatial resolution but easily disturbed by clouds and water vapor to Antarctic ice-sheet near-surface snowmelt detection, this paper builds a screening model and correction model for 89 GHz data with large interference based on the high reliability of AMSR-2 36 GHz data and the high spatial resolution of 89 GHz data and the internal relationship between the two data. The method proposed in this paper was compared with the cross-polarised gradient ratio (XPGR) algorithm and six automatic weather stations. The average detection accuracy of the proposed method and the XPGR algorithm is 97% and 95%, respectively. The experimental results show that the snowmelt detection method proposed in this paper can more accurately detect the snowmelt on the Antarctic Ice Sheet and Ice Shelves.