Automatic Weather Station Data Research Articles

How frequent and which variables of automatic weather station data should be assimilated into WRF-3DVar model? A case study of a squall line event in Beijing

Accurate precipitation forecasting is one of the most challenging problems in mesoscale Numerical Weather Prediction (NWP) models. The utilization of 3-Dimensional Variational (3DVar) Data Assimilation technique based on automatic weather station (AWS) data can significantly enhance the accuracy of precipitation simulations and forecasts using the Weather Research and Forecasting (WRF) model. However, the impacts of different assimilation frequencies and meteorological variables on the accuracy of precipitation forecasts remain unclear. This study comprehensively evaluated the impact of different assimilation frequencies and meteorological variables on precipitation forecasts, as illustrated by a case study of a squall line event on August 2, 2017, in Beijing. Eight experiments were conducted by assimilating different combinations of meteorological variables (air pressure, temperature, relative humidity, wind speed, and wind direction) at various time intervals (1 h, 3 h, and 6 h). The results indicated that the WRF model roughly simulated the evolution of this event but overestimated the precipitation amount, accompanied by a large deviation in precipitation areas. Assimilating detailed AWS data significantly improves the model performance in precipitation forecasts. The experiment assimilating all variables at a 3 h interval yielded the most accurate forecasts, with the maximum threat score (TS) increasing from 0.02 to 0.56. Higher assimilation frequencies do not guarantee a better performance. In practice, a 3 h assimilation frequency emerges as an optimal choice for AWS data assimilation. The assimilation of various variables enhanced precipitation forecasts to varying degrees, with the optimum results achieved when all variables were assimilated. Wind speed and direction were the most significant factors in dynamically enhancing the simulation of precipitation areas. Relative humidity and temperature influenced the precipitation intensity by affecting the evolution of convective precipitation thermodynamically. The findings of this study can contribute to the development of AWS data assimilation strategies for the WRF-3DVar model, thereby enhancing the precipitation forecast accuracy.

The application and modification of WRF-Hydro/Glacier to a cold-based Antarctic glacier

Abstract. The McMurdo Dry Valleys (MDV) are home to a unique microbial ecosystem that is dependent on the availability of freshwater. This is a polar desert and freshwater originates almost entirely from surface and near-surface melt of the cold-based glaciers. Understanding the future evolution of these environments requires the simulation of the full chain of physical processes from net radiative forcing, surface energy balance, melt, runoff and transport of meltwater in stream channels from the glaciers to the terminal lakes where the microbial community resides. To establish a new framework to do this, we present the first application of WRF-Hydro/Glacier in the MDV, which as a fully distributed hydrological model has the capability to resolve the streams from the glaciers to the bare land that surround them. Given that meltwater generation in the MDV is almost entirely dependent on small changes in the energy balance of the glaciers, the aim of this study is to optimize the multi-layer snowpack scheme that is embedded in WRF-Hydro/Glacier to ensure that the feedbacks between albedo, snowfall and melt are fully resolved. To achieve this, WRF-Hydro/Glacier is implemented at a point scale using automatic weather station data on Commonwealth Glacier to physically model the onset, duration and end of melt over a 7-month period (1 August 2021 to 28 February 2022). To resolve the limited energetics controlling melt, it was necessary to (1) limit the percolation of meltwater through the ice layers in the multi-layer snowpack scheme and (2) optimize the parameters controlling the albedo of both snow and ice over the melt season based on observed spectral signatures of albedo. These modifications enabled the variability of broadband albedo over the melt season to be accurately simulated and ensured that modelled surface and near-surface temperatures, surface height change and runoff were fully resolved. By establishing a new framework that couples a detailed snowpack model to a fully distributed hydrological model, this work provides a stepping stone to model the spatial and temporal variability of melt and streamflow in the future, which will enable some of the unknown questions about the hydrological connectivity of the MDV to be answered.

Rainfall variability in Indonesia new capital associated with the Madden-Julian Oscillation and its contribution to flood events

Accurate and current rainfall analysis is crucial for planning and development in the new capital city of Indonesia (IKN). This study examines the effect of the Madden-Julian Oscillation (MJO) on rainfall variability in IKN using a 20-year dataset of Integrated Multi-Satellite Retrievals for GPM (IMERG) version 6 and automatic weather station (AWS) data. The study analyzes flood events in IKN by examining flood information provided by the National Agency for Disaster Countermeasures (BNPB) from 2008 to 2022 and the contribution of the MJO to these events. The findings indicate that the MJO significantly influences rainfall variability in IKN, with a more pronounced effect during the dry season (JJASO) than in the wet season (NDJFMAM). This is demonstrated by the higher occurrence of wet days and increased daily rainfall intensity, which is linked to extreme rainfall and longer-duration events, mainly between midnight and morning (0400–0600 LST). Notably, although floods are more common during the rainy season, the MJO can intensify flood events in IKN during the dry season. Therefore, when developing an effective flood disaster mitigation system for IKN, it is crucial to consider the amplitude and phase of the MJO.

Diurnal Variation Characteristics of Summer Precipitation over the Northern Slope of the Tianshan Mountains, Xinjiang, Northwest China: Basic Features and Responses to the Inhomogeneous Underlying Surface

The diurnal variation characteristics of precipitation in summer (June–August) during the period of 2015–2019 over the Northern Slope of the Tianshan Mountains (NSTM) was analyzed using hourly simulated data from Nanjing University’s real-time forecasting system (WRF_NJU) with 4 km resolution, Automatic Weather Station (AWS) data, and the ERA5-Land data through using methods such as the Rotated Empirical Orthogonal Function (REOF) and Coefficient of Variation (CV). The results show that the diurnal variation pattern of the precipitation over the NSTM simulated by WRF_NJU aligns closely with that of the observational AWS data, and it captured spatial distribution, peak values, and the times of precipitation reasonably well. The hourly precipitation amount (PA), precipitation frequency (PF), and precipitation intensity (PI) all show characteristics of being greater in the afternoon to nighttime than from early morning to noon, and the diurnal variations of precipitation in this region are significantly influenced by altitude. The PA, PF, and PI peak over the southern edge of the Junggar Basin (JB) below 1000 m occurred at around 2200 Local Solar Time (LST). In contrast, peak PA over the mountainous regions above 3000 m occurred at around 1500 LST. Further analysis with REOF and CV indicated that the difference in diurnal variations of precipitation between the mountainous regions and the JB is most pronounced likely due to the topographical influences. The peak PA over the mountainous regions mainly occurred at around 1500 LST, while that of the JB occurred at around 0100 LST. High CV regions for PI are predominantly found over the area near the central JB and the middle Tianshan mountains, whereas high CV regions for the PF are located in the central and northern parts of Urumqi and Changji. In addition, different land surface categories exhibit distinct patterns of diurnal precipitation variation, i.e., the forests, grasslands, and water bodies exhibit their peak PA in the period from early morning to noon, while the impervious surfaces, croplands, and barren lands exhibit their peak PA in the period from afternoon to nighttime.

A Case Study on the Convection Initiation Mechanisms over the Northern Edge of Tarim Basin, Xinjiang, Northwest China

The convection initiation (CI) mechanisms of severe storms have received increasing attention because severe storms have been occurring more frequently around the globe in recent years. In this work, the CI mechanisms of severe convective weather associated with a gust front (GF) which occurred on 9 July 2016, near the Korla at the northern edge of the Tarim Basin, Xinjiang, is investigated using observational data including Doppler weather radar data and automatic weather stations data, and high-resolution numerical simulation data. The results showed that, during the eastward movement of the GF, a number of convective cells were successively triggered in the vicinity of the GF, which developed rapidly and continuously merged with the convective system from behind, resulting in the further development and maintenance of this convective system. According to the diagnostic analysis of vertical acceleration which can be decomposed into dynamic acceleration (ad) and buoyant acceleration (ab), it was found that both ad (up to ~4 × 10−3 m s−2) and ab (up to ~7 × 10−3 m s−2) made positive contributions to the CI. Further analyses based on the decompositions of the ad and ab revealed that the extension term was the main contributor for the ad, while the warming of the dry air due to the release of latent heat from the precipitation condensate made the major contribution to the ab. The extension term indicates the elevated convergence jointly induced by the airflow of mid-level horizontal convective rolls (MHCRs) and updraft flow near the leading edge of the GF. The jointly induced elevated convergent updraft can be considered to be an important contributor for the CI mechanisms.

Spatio-temporal rainfall interception loss at the catchment scale from earth observation in a data-scarce area, Northern Ethiopia

Rainfall interception loss (EI) is an important spatio-temporal variable in hydrological studies. The EI is required as input of integrated hydrological models, which nowadays are increasingly applied in water resources management. However, the EI is typically not considered or arbitrarily simplified, which causes wrong model parameterization and typical overestimation of water resources. Such problem can be particularly consequential in water-limited environments, where EI represents a substantial percentage of rainfall. The EI is rarely quantified experimentally because field data acquisition for its direct estimation is cumbersome and costly, especially at large catchment/basin scales. Hence, Earth Observation Satellites (EOS) can be used as an alternative source of data acquisition to quantify EI, especially at large spatial scales. The main aim of this study was to estimate the daily variability of rainfall interception loss in the data-scarce Zamra catchment (1588 km2) in Ethiopia, applying the EOS solution of the revised Gash model (RGM). Next to EOS, this study utilized four automated weather station data. To test the RGM assumption permitting the use of rainfall as one storm per rainy day (instead of the event-based rainfall), high temporal resolution (15 min) automated weather-station data was used. The test confirmed the validity of that assumption in the Zamra catchment (ZC), so the daily bias-corrected by station data rainfall was used as input. The weather station data was also used to define the event-based ratio of the mean wet-canopy evaporation rate to the mean rainfall rate (Ec¯/R¯) ranging from ∼0.037 to 0.041 at the low-elevated stations to ∼0.063 at the high-elevated station. As the variability of the Ec¯/R¯ of the three low-elevated stations was pretty uniform, the mean 0.04 was assumed for areas below 2000 m a.s.l., but for the areas above, the Ec¯/R¯ was assigned as altitude-dependent applying geographical weighted regression method. Other biophysical parameters, the fractional canopy cover and the canopy storage capacity obtained from leaf area index, were derived from Sentinel 2. The final EI was highly spatially and temporally variable, ranging from zero at bare lands, to 30% of annual rainfall in forested areas and with ∼4% mean of annual rainfall for the whole ZC. While applying EOS solution of RGM, facilitated with limited amount of ground-based data, the presented study offers an efficient and realistic way of spatio-temporal quantification of EI, particularly suitable for large data-scarce areas such as the ZC.

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.

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

The interactions between urban heat island and heat waves amplify urban warming in Guangzhou, China: Roles of urban ventilation and local climate zones

Under the background of global warming, interaction between heat waves (HWs) and urban heat island (UHI) has led to trends of increase in the intensity, frequency, and duration of extreme heat events in urban areas, seriously threatening the health of urban populations. Taking Guangzhou (a tropical megacity in China) as an example, this study used automatic weather station data and ERA5 reanalysis data to explore the interaction between HWs and UHI, and to elucidate the effects of wind speed and local climate zones (LCZs) on such interaction. Results revealed obvious HWs–UHI interaction in Guangzhou, whereby HWs induces an amplification effect on UHI intensity (UHII) that was most significant at night. In the main urban area, UHII and HWs both weakened with increasing wind speed, indicating that low wind speeds contribute to increased occurrence of HWs and enhancement of UHII. Differently, in some areas peripheral to the main urban area, the UHII at medium wind speeds was stronger than that at low wind speeds, which reflect the impact of heat advection from the urban center. For different LCZs in the main urban area, the strongest UHII, highest risk of HW occurrence, and most significant HWs–UHI interaction were found in the compact mid-rise buildings and compact low-rise buildings (LCZ2 and LCZ3, respectively), followed by the compact high-rise buildings (LCZ1), which was mainly affected by the shading effect of high-rise buildings. The weakest UHII and lowest risk of HW occurrence were found in open high-rise buildings and open mid-rise buildings (LCZ4 and LCZ5, respectively), which generally have good ventilation conditions. Our findings will help to understand urban warming and its association with UHI and HW events in tropical urban regions, which has implications for rational improvement of the urban thermal environment in other tropical urban regions globally.

The surface energy balance during foehn events at Joyce Glacier, McMurdo Dry Valleys, Antarctica

Abstract. The McMurdo Dry Valleys (MDV) are a polar desert, where glacial melt is the main source of water to streams and the ecosystem. Summer air temperatures are typically close to zero, and therefore foehn events can have a large impact on the meltwater production. A 14-month record of automatic weather station (AWS) data on Joyce Glacier is used to force a 1D surface energy balance model to study the impact of foehn events on the energy balance. AWS data and output of the Antarctic Mesoscale Prediction System (AMPS) on a 1.7 km grid are used to detect foehn events at the AWS site. Foehn events at Joyce Glacier occur under the presence of cyclones over the Ross Sea. The location of Joyce Glacier on the leeward side of the Royal Society Range during these synoptic events causes foehn warming through isentropic drawdown. This mechanism differs from the foehn warming through gap flow that was earlier found for other regions in the MDV and highlights the complex interaction of synoptic flow with local topography of the MDV. Shortwave radiation is the primary control on melt at Joyce Glacier, and melt often occurs with subzero air temperatures. During foehn events, melt rates are enhanced, contributing to 23 % of the total annual melt. Foehn winds cause a switch from a diurnal stability regime in the atmospheric surface layer to a continuous energy input from sensible heat flux throughout the day. The sensible heating during foehn, through an increase in turbulent mixing resulting from gustier and warmer wind conditions, is largely compensated for by extra heat losses through sublimation. Melt rates are enhanced through an additional energy surplus from a reduced albedo during foehn.

Integrated use of regional weather forecasting and crop modeling for water stress assessment on rice yield

This study evaluated the effects of water stress on rice yield over Punjab and Haryana across North India by integrating Weather Research Forecasting (WRF) and Decision Support System for Agrotechnology Transfer (DSSAT) models. Indian Remote Sensing Satellite datasets were used to define land use/land cover in WRF. The accuracy of simulated rainfall and temperature over Punjab and Haryana was evaluated against Tropical Rainfall Measuring Mission and automated weather station data of Indian Space Research Organization, respectively. Data from WRF was used as weather input to DSSAT to simulate rice yield in Punjab and Haryana for 2009 and 2014. After simulated yield has been evaluated against district-level observed yield, the water balance components within the DSSAT model were used to analyze the impact of water stress on rice yield. The correlation (R2) between the crop water stress factor and the rice yield anomaly at the vegetative and reproductive stage was 0.64 and 0.52 for Haryana and 0.73 and 0.68 for Punjab, respectively. Severe water stress during the flowering to maturity stage inflicted devastating effects on yield. The study concludes that the regional climate simulations can be potentially used for early water stress prediction and its impact on rice yield.

ADT: The automatic weather station data tool

Climate data are essential in an array of climate research and applications. Climate data also provide the foundation for the provision of climate services. However, in many parts of Africa, weather stations are sparse, and their numbers have been declining over the last half-century. Moreover, the distribution of existing meteorological stations is uneven, with most weather stations located in towns and cities along the main roads. To address these data gaps, efforts over the last decade, largely driven by external donor funding, have focused on expanding meteorological observation networks in many parts of Africa, mainly through the provision of Automatic Weather Stations (AWS) to National Meteorological Services (NMS). While AWS offer a number of advantages over the conventional ones, which include automated reporting at a very fine temporal resolution (15 min, on average), they also have several disadvantages and accompanying challenges to their use. Some of these well-known challenges are the high maintenance requirements and associated costs that arise from the need to procure replacement parts that may not be available locally. However, another major, under-discussed challenge confronting NMS is the disparities between the different station types provided by different donors that has given rise to barriers to pragmatically using the plethora of data collected by AWS in decision-making processes. These disparities include major differences in the way the data from various AWS types are formatted and stored, which result in poorly coordinated, fragmented, and unharmonized datasets coming from different AWS networks. The end result is that while top-of-the-line AWS networks may systematically be collecting highly needed data, the inability of NMS to efficiently, combine, synchronize, and otherwise integrate these data coherently in their databases limits their use. To address these challenges, a free web-based application called Automatic Weather Station Data Tool (ADT) with an easy-to-use graphical user interface was developed to help NMS to access, process, perform quality control, and visualize data from different AWS networks in one place. Now implemented in five African countries (Ethiopia, Ghana, Kenya, Rwanda, and Zambia), ADT also enables real-time monitoring of stations to see which ones are working and which ones are offline. This tool emerged from a wider climate services approach, the Enhancing National Climate Services (ENACTS), recognizing that availability of high-quality climate data does not automatically translate to ease of access or effective use.

Prediction of near-surface air temperature in glacier regions using ERA5 data and the random forest regression method

Near-surface air temperature is an essential physical parameter for estimating the glacier ablation model that is significant to understanding the dynamics of the changes and constant monitoring of glaciers. There is a significant difference between the temperatures in glacier and non-glacier areas at the same altitude, and the estimation model that uses low-altitude automatic weather station (AWS) data cannot be applied to glaciers far away from the stations. In this study, a scheme for air temperature prediction using the random forest regression (RFR) model and the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5 (ERA5) data was analyzed. The results show that the average mean absolute error (MAE) and root mean square error (RMSE) of the ERA5 raw temperature product for three glaciers were 4.32 °C and 5.72 °C, respectively. After the RFR model revision, the MAE and RMSE significantly decreased to 1.01 °C and 1.41 °C, respectively. In addition, when the Linzhi weather station was used to calculate the temperature in the Parlung No. 4 Glacier area, the MAE of the Greuell and Böhm (GB) model decreased by 0.18 °C compared to that obtained using the lapse rate method. With our method, long time series of glacier surface air temperature data with high accuracy can be obtained by using short term in-situ temperature measurements.

Challenges in modeling the energy balance and melt in the percolation zone of the Greenland ice sheet

AbstractIncreased surface melt in the percolation zone of the Greenland ice sheet causes significant changes in the firn structure, directly affecting the amount and timing of meltwater runoff. Here we force an energy-balance model with automatic weather stations data at two sites in the percolation zone of southwest Greenland ($2040$ and 2360 m a.s.l.) between spring $2017$ and fall $2019$. Extensive model validation and sensitivity analysis reveal that the skin layer formulation used to compute the surface temperature by closing the energy balance leads to a consistent overestimation of melt by more than a factor of two or three depending on the site. In contrast, model results match the observations well when the model is forced by observed surface temperatures; however, unexplained residuals in the energy balance occur. The sensible and ground heat flux differ markedly in the two simulations accounting largely for the difference in modeled melt amounts. This indicates that the energy available for melt is highly sensitive to small changes in surface temperature. Thus, regional climate models that also use the skin layer formulation may have a bias in surface temperature and melt energy in the percolation zone of the ice sheet.

Long‐term (2001–2020) trend analysis of temperature and rainfall and drought characteristics by in situ measurements at a tropical semi‐arid station from southern peninsular India

AbstractWith the effects of climate change prevailing worldwide, it is essential to understand the long‐term rainfall and temperature trends at regional scales to plan for adaptation strategies. Investigating the temporal dynamics of meteorological variables in climate‐induced changes, particularly in rain‐fed agriculture countries such as India, is needful. The present study focused on the temporal variation of rainfall and temperature trends to analyse the drought characteristics over the past 20 years (2001–2020) using ground‐based Automatic Weather Station (AWS) data at a semi‐arid station Anantapur in southern peninsular India. The seasonal maximum mean temperature (Tmean) was observed in summer (32.20 ± 1.36°C), followed by the monsoon (29.08 ± 1.02°C), postmonsoon (26.45 ± 2.44°C), and winter (25.45 ± 1.01°C) during the study period. The Mann–Kendall trend test result showed the decreasing trend in seasonal mean temperature during the drying period (winter [−0.069°C·year−1] and summer [−0.081°C·year−1]) and increasing trend in the wetting period (monsoon [0.012°C·year−1] and postmonsoon [0.034°C·year−1]). On the other hand, the seasonal rainfall decreases in the postmonsoon (−2.276 mm·year−1) while increasing in winter (0.5 mm·year−1), summer (2.137 mm·year−1), and monsoon (6.901 mm·year−1) seasons. The annual trends of minimum, maximum, and mean monthly temperatures decrease at the rate of −0.121, −0.123, and −0.022°C·year−1, respectively, while the rainfall increases at the rate of 8.979 mm·year−1. Furthermore, the temporal evolution of meteorological drought characteristics was estimated with the help of the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) on multiple timescales ranging from 1 to 24 months on a monthly basis during the study period. The 24‐month SPI values showed that 1 “severely dry” and the 29 “moderately dry” long‐term drought events were observed. From 24‐month SPEI, a total of 15 “severely dry” and 20 “moderately dry” long‐term droughts were observed during the study period. The severely dry years were observed in 2002 and 2003, while the wettest periods were 2007 and 2010. Both indices are identified as more severe dry periods from 2002 to 2006 and 2013 to 2017 than rest periods. Also, a significant decreasing tendency of droughts was observed in both indices. The SPI values are compared with El Niño–Southern Oscillation (ENSO) and normalized difference vegetation index (NDVI) and found that the SPI versus ENSO index negatively correlates with the coefficient of −0.58 significantly, while the SPI versus NDVI correlation is insignificant. The study region converted to wetter conditions during the last 20 years.

Fine structure analysis of urban heat island of a central city in low-latitude plateau of China

High-resolution automatic weather station data and remote sensing data were used to analyze the canopy urban heat island (UHI) effect in Kunming, a central city on a low-latitude plateau in China, and its relationship with land cover types, anthropogenic heat, and meteorological conditions. Results showed that the UHI intensity exponentially decreased with the distance from downtown regions. Furthermore, a forward stepwise regression algorithm was used to assess the contribution of spatial factors (e.g., land cover types and population density) and temporal factors (e.g., meteorological factors) to UHI changes. The contributions of spatial factors to the patterns of nighttime (daytime) UHI intensity reached 61.5% (32.6%). By contrast, the contributions of temporal factors to the temporal changes of nighttime (daytime) UHI varied considerably throughout the seasons, characterized by a maximum in autumn 79.70% (28.00%) and a minimum in spring 57.90% (12.00%). The temporal and spatial factors jointly determined the spatiotemporal patterns of the UHI effects. The temporal variation of spatial factors likewise affected the temporal variation of UHI intensity, whereas temporal factors did not change the spatial distribution of UHI intensity but aggravated its heterogeneity.

Detection of the impact of a tropical cyclonic system on the dynamics and energetics of the atmosphere using wind profiler radar

Rapid changes in the tropospheric circulation features associated with the overhead passage of the Gaja cyclonic system over the 205 MHz Stratosphere Troposphere wind profiler radar observations at Kochi (10.03° N, 76.33° E), India, have been studied. The severe cyclonic system formed in the southeast Indian Peninsular region weakened into a depression after landfall near the Tamil Nadu coast. On 16th November 2018, the cyclonic system crossed the Western Ghats and travelled westward at 33 knots over the ST radar site at Kochi in the evening. Later it reached the Arabian Sea and intensified again into a severe cyclone. Continuous observations of the vertical structure of the wind pattern at 4-min intervals from the wind profiler radar have been examined. The impact of the transit of the cyclonic system extends up to a height of 13 km in the atmosphere. The vertical distribution of turbulent kinetic energy in the atmosphere indicates a sudden disruption in the tropospheric levels at the time of storm passage. The cyclonic system traversed over the Western Ghats positioned at an altitude of 2500 m. It crossed the radar site at the mean sea level after passing a horizontal distance of 100 km. The abrupt changes in the topographical conditions generate atmospheric gravity waves in the leeward side of the Western Ghats, as observed from the ST radar, are presented. During the period, changes in surface parameters were evaluated using co-located automatic weather station (AWS) data. Satellite information and Doppler weather radar observations from Kochi have also supplemented the investigation.

Evaluation of air temperature estimated by ERA5-Land reanalysis using surface data in Pernambuco, Brazil.

This study aimed to analyze the average air temperature data estimated by ERA5-Land reanalysis over 10years (01/01/2011 to 12/31/2020), based on data from 12 automatic weather stations located in different cities in the state of Pernambuco, northeast of Brazil. For more careful evaluation, the average air temperature data were stratified by mesoregions of the state. ERA5-Land reanalysis data were validated using statistical indices that evaluated the accuracy. The average air temperature estimated by ERA5-Land reanalysis agree well with weather stations in almost the entire state of Pernambuco. The highest accuracy of the average air temperature estimated by ERA5-Land occurred in the city of Ibimirim (R2 = 0.98), while the lowest accuracy was measured in the city of Caruaru (R2 = 0.57). Root-mean-square error (RMSE) generated by the ERA5-Land reanalysis was lower than 0.60°C in most Pernambuco. The highest RMSE of ERA5-Land for average air temperature was calculated using data from the city of Caruaru (1.11°C), whereas the lowest RMSE was obtained with data from the city of Recife (0.41°C). According to mean bias errors, (MBE) the ERA5-Land underestimated the average air temperature data compared to automatic weather stations data, especially in Ouricuri, which had the highest underestimation (-0.80°C). On the other hand, among the municipalities where ERA5-Land overestimated the temperature values, the highest overestimation was identified in Garanhuns (0.35°C). Therefore, based on the results of this study, ERA5-Land reanalysis successfully estimated the average air temperature for the state of Pernambuco.

Forecast Characteristics of Radar Data Assimilation Based on the Scales of Precipitation Systems

Radar data with high spatiotemporal resolution and automatic weather station (AWS) data are used in the data assimilation experiment to improve the precipitation forecast of a numerical model. The numerical model considered in this study is the Weather Research and Forecasting (WRF) model with double-moment 6-class microphysics scheme (WDM6). We calculated the radar equivalent reflectivity factor using high resolution WRF and compared it with radar observations in South Korea. To compare the precipitation forecast characteristics of the three-dimensional variational (3D-Var) assimilation of radar data, four experiments were performed based on the scales of precipitation systems. Comparison of the 24 h accumulated rainfall with surface observation data, contoured frequency by altitude diagram (CFAD), time–height cross sections (THCS), and vertical hydrometeor profiles was used to evaluate the accuracy of the simulation of precipitation. The model simulations were performed with and without 3D-VAR radar reflectivity, radial velocity and AWS assimilation for two mesoscale convective cases and two synoptic scale cases. The combined effect of the radar and AWS data assimilation experiment improved the location of the precipitation area and rainfall intensity compared to the control run. There is a noticeable scale dependence in the improvement of precipitation systems. Improvements in simulating mesoscale convective systems were larger compared to synoptically driven precipitation systems.

Ensemble-based Model for Rainfall Nowcasting using Automatic Weather Station Data

Ensemble-based Model for Rainfall Nowcasting using Automatic Weather Station Data