Automatic Weather Stations Research Articles

Weather Data Management Using Hadoop MapReduce in Zimbabwe

The Meteorological Services Department (MSD) in Zimbabwe is experiencing huge costs in transferring data of over Gigabytes from its different models of automatic weather stations into centralized database at Head Quarters (HQ) Belvedere in Harare. However, surveys indicate that emerging technologies of big data analytics such as Hadoop MapReduce can be extended into weather data management offering multiplicity of functions that includes: prediction, management of transmission of data and also managing the range of weather elements to monitor at anytime and anywhere. This study therefore sought to design and develop an experimental set-up to understand how Hadoop MapReduce can be used to manage data files from various weather stations simulated as Application Programming Interface (APIs). The developed system was conducted entirely in a cloud environment and the results suggest that Hadoop MapReduce function is an appropriate big data tool to manage weather data and offers better options especially for cash strapped organizations such as Meteorological Services Department (MSD). Adoption of MapReduce is economic and elastic to accord changing weather data management requirements. The research used a small dataset of 100 records which might offer comprehensive model for use for practical purposes. Further studies with more realistic huge datasets might be pertinent.

A long-term mass-balance reconstruction (1974–2021) and a decadal in situ mass-balance record (2011–2021) of Rikha Samba Glacier, central Himalaya

AbstractDespite their importance for regional water resource planning and as indicators of climate change, records of in situ glacier mass balance remain short and spatially sparse in the Himalaya. Here, we present an updated series of in situ mass-balance measurements from Rikha Samba Glacier, Nepal, between 2011 and 2021. The updated in situ mass balance is −0.39 ± 0.32 m w.e. for this period. We use an energy-mass balance model to extend the annual mass-balance series back to 1974. The model is forced using daily meteorological variables from ERA5-Land reanalysis data that is linearly bias-corrected using observations from an automatic weather station situated near the glacier terminus. The modeled mass balance is consistent with the in situ mass-balance series measured 2011–2021 and with previous glaciological and geodetic estimates. The model results indicate a mass balance of −0.56 ± 0.27 m w.e. a−1over the reconstruction period of 1974–2021, which is comparable to the mass losses experienced by other Himalayan glaciers during this time. An assessment of the sensitivity of the glacier mass balance to meteorological forcing suggests that a change in temperature of ±1 K has a stronger effect on the calculated mass balance compared to a ±20% change in either precipitation, or relative humidity, or solar radiation.

Reconstruction of Near-Surface Air Temperature over the Greenland Ice Sheet Based on MODIS Data and Machine Learning Approaches

High spatial and temporal resolution products of near-surface air temperature (T2m) over the Greenland Ice Sheet (GrIS) are required as baseline information in a variety of research disciplines. Due to the sparse network of in situ data on the GrIS, remote sensing data and machine learning methods provide great advantages, due to their capacity and accessibility. The Land Surface Temperature (LST) at 780 m resolution from the Moderate Resolution Imaging Spectroradiometer (MODIS) and T2m observation from 25 Automatic Weather Stations (AWSs) are used to establish a relationship over the GrIS by comparing multiple machine learning approaches. Four machine learning methods—neural network (NN), gaussian process regression (GPR), support vector machine (SVM), and random forest (RF)—are used to reconstruct the T2m at daily and monthly scales. We develop a reliable T2m reconstruction model based on key meteorological parameters, such as albedo, wind speed, and specific humidity. The reconstructions daily and monthly products are generated on a 780 m × 780 m spatial grid spanning from 2007 to 2019. When compared with in situ observations, the NN method presents the highest accuracy, with R of 0.96, RMSE of 2.67 °C, and BIAS of −0.36 °C. Similar to the regional climate model (RACMO2.3p2), the reconstructed T2m can better reflect the spatial pattern in term of latitude, longitude, and altitude effects.

The PANDA automatic weather station network between the coast and Dome A, East Antarctica

Abstract. This paper introduces a unique multiyear dataset and the monitoring capability of the PANDA automatic weather station network, which includes 11 automatic weather stations (AWSs) across the Prydz Bay–Amery Ice Shelf–Dome A area from the coast to the summit of the East Antarctic Ice Sheet. The ∼ 1460 km transect from Zhongshan to Panda S follows roughly along ∼ 77∘ E longitude and covers all geographic units of East Antarctica. Initial inland observations, near the coast, started in the 1996/97 austral summer. All AWSs in this network measure air temperature, relative humidity, air pressure, wind speed and wind direction at 1 h intervals, and some of them can also measure firn temperature and shortwave/longwave radiation. Data are relayed in near real time via the Argos system. The data quality is generally very reliable, and the data have been used widely. In this paper, we firstly present a detailed overview of the AWSs, including the sensor characteristics, installation procedure, data quality control protocol and the basic analysis of each variable. We then give an example of a short-term atmospheric event that shows the monitoring capacity of the PANDA AWS network. This dataset, which is publicly available, is planned to be updated on a near-real-time basis and should be valuable for climate change estimation, extreme weather events diagnosis, data assimilation, weather forecasting, etc. The dataset is available at https://doi.org/10.11888/Atmos.tpdc.272721 (Ding et al., 2022b).

Spatiotemporal change analysis for snowmelt over the Antarctic ice shelves using scatterometers

Using Scatterometer-based backscatter data, the spatial and temporal melt dynamics of Antarctic ice shelves were tracked from 2000 to 2018. We constructed melt onset and duration maps for the whole Antarctic ice shelves using a pixel-based, adaptive threshold approach based on backscatter during the transition period between winter and summer. We explore the climatic influences on the spatial extent and timing of snowmelt using meteorological data from automatic weather stations and investigate the climatic controls on the spatial extent and timing of snowmelt. Melt extent usually starts in the latter week of November, peaks in the end of December/January, and vanishes in the first/second week of February on most ice shelves. On the Antarctic Peninsula (AP), the average melt was 70 days, with the melt onset on 20 November for almost 50% of the region. In comparison to the AP, the Eastern Antarctic experienced less melt, with melt lasting 40–50 days. For the Larsen-C, Shackleton, Amery, and Fimbul ice shelf, there was a substantial link between melt area and air temperature. A significant correlation is found between increased temperature advection and high melt area for the Amery, Shackleton, and Larsen-C ice shelves. The time series of total melt area showed a decreasing trend of −196 km2/yr which was statistical significant at 97% interval. The teleconnections discovered between melt area and the combined anomalies of Southern Annular Mode and Southern Oscillation Index point to the high southern latitudes being coupled to the global climate system. The most persistent and intensive melt occurred on the AP, West Ice Shelf, Shackleton Ice Shelf, and Amery Ice Shelf, which should be actively monitored for future stability.

Analysis of the Microphysical Structure and Evolution Characteristics of a Typical Sea Fog Weather Event in the Eastern Sea of China

This study is the first to use the observation data of a fog monitor, a visibility meter, and an automatic weather station to carry out a comprehensive observation experiment from the perspective of microphysics on a severe sea fog process in Beilun District, China, from 14 to 15 June 2021. The results show the following: (1) Temperature is closely related to nucleation, condensation growth, and other processes. The decrease (increase) in temperature is the main reason for the enhancement (weakening) of nucleation and the growth of condensation (evaporation of droplets), which leads to an increase (or decrease) in microphysical quantities, such as droplet number concentration and liquid water content. (2) The average droplet number spectral distribution roughly conforms to the Gamma distribution, and the spectral distribution of the fog process presents a ”multi-peak” structure, with peak diameters of 6 μm, 12 μm, 16 μm, 24 μm, and 44 μm. Droplets with a diameter of less than 16 μm account for 75% of the droplet size distribution. (3) During this sea fog process, three microphysical parameters, namely, number concentration, liquid water content, and average diameter, are all positively correlated in pairs, but the positive correlation between the number concentration and the average diameter is weak. This shows that the condensation nucleation and the condensation growth of droplets are the main processes in this sea fog process and that the collision process occurs but is not the dominant process. The sea fog comprehensive observation experiment provides an important demonstration of the microphysics research of sea fog in the eastern coastal areas of China and provides more reference information for sea fog research and equipment comparisons between different regions. At the same time, it also provides an essential scientific basis for the short-term forecast of sea fog in the future and for the optimization of the microphysical parameters of related models.

Performance assessment of gridded snow products in the Upper Euphrates

Snow observations are important in many mountain areas to quantify the water stored in snowpacks and to predicting runoff during the melting period. In this study we compare the performance of five different regional-scale gridded snow products to reproduce snow water equivalent (SWE) in the Upper Euphrates region (Karasu Basin, 10,275 km2), with observations from automatic weather stations in the catchment through Taylor diagrams. The products compared are the ERA5, ERA5-Land, MERRA-2, snow data from a dynamical downscaling of ERA-5 (period 2000-2018) and SWE generated from microwave satellite data (SWE-E(H13) period 2013-2015 product of the EUMETSAT H SAF project). The H13 product presented deficiencies in terms of not being able to reproduce the spatial and temporal variability of the snowpack. ERA-5 and, in particular, ERA-Land products, at 30 and 9 km grid size, respectively, showed good performance in reproducing snow evolution compared to four available observation sites. MERRA2 at 50 km resolution showed lower skills compared to the above-mentioned products. Resulting snow data from WRF at 10 km resolution did not show any improvement with respect to the global datasets. The impossibility of testing different configurations due to the lack of observations to compare and the computational constraints to test different parametrizations may be the reasons to explain the low performance although they remain speculative. All the gridded datasets showed good performance in reproducing snow duration over the basin, compared to remotely sensed data. Results highlight ERA-Land dataset as a very promising tool for regional snow studies in mountainous regions with limited observations, in a cost-effective way.

Performance of the Improved Priestley-Taylor Model for Simulating Evapotranspiration of Greenhouse Tomato at Different Growth Stages.

Mastering crop evapotranspiration (ET) and improving the accuracy of ET simulation is critical for optimizing the irrigation schedule and saving water resources, particularly for crops cultivated in a greenhouse. Taking greenhouse-grown tomato under drip irrigation as an example, two weighing lysimeters were used to monitor ET at two seasons (2019 and 2020), whilst meteorological factors inside the greenhouse were measured using an automatic weather station. Then the path analysis approach was employed to determine the main environmental control factors of ET. On this basis, an improved Priestley-Taylor (IPT) model was developed to simulate tomato ET at different growth stages by considering the influence of environmental changes on model parameters (e.g., leaf senescence coefficient, temperature constraint coefficient and soil evaporative water stress coefficient). Results showed that the average daily ET varied from 0.06 to 6.57 mm d-1, which were ~0.98, ~2.58, ~3.70 and ~3.32 mm/d at the initial, development, middle and late stages, respectively, with the total ET over the whole growth stage of ~333.0 mm. Net solar radiation (Rn) and vapor pressure deficit (VPD) were the direct influencing factors of ET, whereas air temperature (Ta) was the limiting factor and wind speed (u2) had a little influence on ET. The order of correlation coefficients between meteorological factors and ET at two seasons was Rn > VPD > Ta > u2. The IPT model can accurately simulate ET in hourly and daily scales. The root mean square error of hourly ET at four stages changed from 0.002 to 0.08 mm h-1 and daily ET varied from 0.54 to 0.57 mm d-1. The IPT coefficient was close to the recommended PT coefficient (1.26) when the average Ta approaches 26 °C and LAI approaches 2.5 cm2 cm-2 in greenhouse conditions. Our results can provide a theoretical basis for further optimization of greenhouse crop irrigation schedules and improvement of water use efficiency.

Using the SCOPE model for potato growth, productivity and yield monitoring under different levels of nitrogen fertilization

Most applications of remote sensing in agricultural crop monitoring use multispectral imaging techniques, but with upcoming hyperspectral missions, the opportunity arises to better estimate pigment absorption and crop structure by exploiting the full solar reflective spectrum. In this study, we demonstrate how hyperspectral time series can be used with the Soil Canopy Observation of Photochemistry and Energy fluxes (SCOPE) model to estimate crop yield variability among fields, crop varieties and nitrogen treatments generically, i.e. without a calibration with in situ , data. Field experiments were conducted in two potato fields in the Netherlands between May and September 2019. The fields were planted with five varieties of potato, under three nitrogen fertilization treatments. By fitting the model to the full VNIR-SWIR spectrum of measured hyperspectral reflectance, we retrieved the model input parameters of Leaf Area Index (LAI), leaf chlorophyll content (Cab) and leaf water content (Cw) and simulated the photosynthesis throughout the season using data of local Automatic Weather Stations (AWS). Statistical analysis of measured and retrieved traits of LAI, Cab and canopy water content showed that two fields responded differently to the treatments, exhibiting fewer classes than were expected based on the experimental design. Potato yield, which was estimated as the sum of photosynthesis flux multiplied by the harvest index of 0.64, correlated with the measured tuber dry weight with R 2 0.36 and RMSE 2.5 t ha −1 . This study demonstrates that even in the absence of crop or variety specific information, hyperspectral reflectance and local weather data ingested into SCOPE can explain a substantial part of the observed variability in yield among fields. • Time series of hyperspectral canopy reflectance were recorded in two potato fields. • The variation in traits was created by nitrogen fertilization treatments. • SCOPE model retrieved plant traits contained information about treatments. • SCOPE-simulated accumulated photosynthesis correlated with the final dry yield. • SCOPE model is more generic than dimensionality reduction methods.

Using NDVI, climate data and machine learning to estimate yield in the Douro wine region

Estimating vineyard yield in advance is essential for planning and regulatory purposes at the regional level, with growing importance in a long-term scenario of perceived climate change. With few tools available, the current study aimed to develop a yield estimation model based on remote sensing and climate data with a machine-learning approach. Using a satellite-based time-series of Normalized Difference Vegetation Index (NDVI) calculated from Sentinel 2 images and climate data acquired by local automatic weather stations, a system for yield prediction based on a Long Short-Term Memory (LSTM) neural network was implemented. The study was conducted in the Douro Demarcated Region in Portugal over the period 2016–2021 using yield data from 169 administrative areas that cover 250,000 ha, in which 43,000 ha of the vineyard are in production. The optimal combination of input features, with an Mean Absolute Error (MAE) of 672.55 kg/ha and an Mean Squared Error (MSE) of 81.30 kg/ha, included the NDVI, Temperature, Relative Humidity, Precipitation, and Wind Intensity. The model was tested for each year, using it as the test set, while all other years were used as input to train the model. Two different moments in time, corresponding to FLO (flowering) and VER (veraison), were considered to estimate in advance wine grape yield. The best prediction was made for 2020 at VER, with the model overestimating the yield per hectare by 8 %, with the average absolute error for the entire period being 17 %. The results show that with this approach, it is possible to estimate wine grape yield accurately in advance at different scales.

Degree Days as a Method to Estimate the Optimal Harvest Date of ‘Conference’ Pears

The European pear is a climacteric fruit species characterised by high storability. The ‘Conference’ pear can be stored in a CA (controlled atmosphere) room without major losses for half a year. However, for ‘Conference’ storage to be successful, the fruit must be harvested when fully developed before it enters the climatic maturation phase. Among the various methods used to determine the optimal harvest date, the degree-day method, which relies on the sum of active temperatures, seems to be relatively accurate and easy to apply. During 11 years of study, pears were harvested on four dates (every 4–5 days) chosen based on measurements and observations, and then, after a six-month storage period, the optimal harvest date was determined. During the growing season, the temperature was measured using an automatic weather station to calculate the sum of active temperatures from full bloom to the optimal harvest date. Measurements made after storage were analysed, and the sum of active temperatures needed for the proper development of the ‘Conference’ pear was calculated to be 2469 degree days. The calculations are very accurate because the dispersion calculated using the standard deviation was only 20°. The base temperature with the smallest error was 0 °C. This study shows that this method can be recommended as useful for determining the optimal harvest date.

Impacts and Climate Change Adaptation of Agrometeorological Services among the Maize Farmers of West Tamil Nadu

Climate change is often linked with record-breaking heavy or poor rainfall events, unprecedented storms, extreme day and night time temperatures, etc. It may have a marked impact on climate-sensitive sectors and associated livelihoods. Block-level weather forecasting is a new-fangled dimension of agrometeorological services (AAS) in the country and is getting popularized as a climate-smart farming strategy. Studies on the economic impact of these microlevel advisories are uncommon. Agromet advisory services (AAS) play a critical role as an early warning service and preparedness among the maize farmers in the Parambikulam–Aliyar Basin, as this area still needs to widen and deepen its AWS network to reach the village level. In this article, the responses of the maize farmers of Parambikulam–Aliyar Basin on AAS were analyzed. AAS were provided to early and late Rabi farmers during the year 2020–2022. An automatic weather station was installed at the farmers’ field to understand the real-time weather. Forecast data from the India Meteorological Department (IMD) were used to provide agromet advisory services. Therefore, the present study deserves special focus. Social media and other ICT tools were used for AAS dissemination purposes. A crop simulation model (CSM), DSSAT4.7cereal maize, was used for assessing maize yield in the present scenario and under the elevated GHGs scenario under climate change. Our findings suggest that the AAS significantly supported the farmers in sustaining production. The AAS were helpful for the farmers during the dry spells in the late samba (2021–2022) to provide critical irrigation and during heavy rainfall events at the events of harvest during early and late Rabi (2021–22). Published research articles on the verification of weather forecasts from South India are scanty. This article also tries to understand the reliability of forecasts. Findings from the verification suggest that rainfall represented a fairly good forecast for the season, though erratic, with an accuracy score or HI score of 0.77 and an HK score of 0.60, and the probability of detection (PoD) of hits was found to be 0.91. Verification shows that the forecasted relative humidity observed showed a fairly good correlation, with an R2 value of 0.52. These findings suggest that enhancing model forecast accuracy can enhance the reliability and utility of AAS as a climate-smart adaptation option. This study recommends that AAS can act as a valuable input to alleviate the impacts of hydrometeorological disasters on maize crop production in the basin. There is a huge demand for quality weather forecasts with respect to accuracy, resolution, and lead time, which is increasing across the country. Externally funded research studies such as ours are an added advantage to bridge the gap in AAS dissemination to a great extent.

Performance evaluation of evapotranspiration estimation methods in Sultanpur, Uttar Pradesh, India

Accurate estimation of evapotranspiration plays a vital role in judicious irrigation scheduling. Keeping this in view, a study was undertaken to compare the performance of five methods, viz. Penman method (PM), FAO Penman Monteith (FAO-PM), Priestley-Taylor (PT), FAO Radiation method (FAO-RM) and Hargreaves methods (HM) for Sultanpur District of Uttar Pradesh, India using the daily weather data acquired from automatic weather station during 2016-17. The performance evaluation of selected methods was carried out using linear regression and simple statistical analysis to suggest a substitute of FAO-PM for estimation of reference evapotranspiration using minimal climatic parameters available at regional scales. It was observed that the PM method performed the best and was in line with estimated ET0 by FAO-PM method with coefficient of determination (R2), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Modified Nash-Sutcliffe Coefficient (EMNS), Percent Bias (PBIAS) estimated as 0.94, 0.54, 0.40, 0.73, 3.74 for the year 2016 and 0.95, 0.44, 0.36, 0.73, 6.01 in 2017, respectively. Nonetheless, it was observed form this study that the PM method which requires four parameters, viz. daily solar radiation, maximum and minimum air temperature, and wind speed can be a substitute to FAO-PM that require more parameters for ET0 estimation in data scarce situations.

Performance Assessment of GPM IMERG Products at Different Time Resolutions, Climatic Areas and Topographic Conditions in Catalonia

Quantitative Precipitation Estimates (QPEs) from the Integrated Multisatellite Retrievals for GPM (IMERG) provide crucial information about the spatio-temporal distribution of precipitation in semiarid regions with complex orography, such as Catalonia (NE Spain). The network of automatic weather stations of the Meteorological Service of Catalonia is used to assess the performance of three IMERG products (Early, Late and Final) at different time scales, ranging from yearly to sub-daily periods. The analysis at a half-hourly scale also considered three different orographic features (valley, flat and ridgetop), diverse climatic conditions (BSk, Csa, Cf and Df) and five categories related to rainfall intensity (light, moderate, intense, very intense and torrential). While IMERG_E and IMERG_L overestimate precipitation, IMERG_F reduces the error at all temporal scales. However, the calibration to which a Final run is subjected causes underestimation regardless in some areas, such as the Pyrenees mountains. The proportion of false alarms is a problem for IMERG, especially during the summer, mainly associated with the detection of false precipitation in the form of light rainfall. At sub-daily scales, IMERG showed high bias and very low correlation values, indicating the remaining challenge for satellite sensors to estimate precipitation at high temporal resolution. This behaviour was more evident in flat areas and cold semi-arid climates, wherein overestimates of more than 30% were found. In contrast, rainfall classified as very heavy and torrential showed significant underestimates, higher than 80%, reflecting the inability of IMERG to detect extreme sub-daily precipitation events.

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.

Hourly assimilation of different sources of observations including satellite radiances in a mesoscale convective system case during RELAMPAGO campaign

This paper evaluates the impact of assimilating high-resolution surface networks and satellite observations using the WRF-GSI-LETKF over central and north eastern Argentina where the surface and upper air observing networks are relatively coarse. A case study corresponding to a huge mesoscale convective system (MCS) that developed during November 22, 2018 was used. The accumulated precipitation associated with this MCS was quite high, exceeding 200 mm over northern Argentina and Paraguay. The MCS developed during the Intense Observing Period (IOP) of the Remote sensing of Electrification, Lightning, And Mesoscale/microscale Processes with Adaptive Ground Observations (RELAMPAGO) field campaign. The GSI-4DLETKF data assimilation package is used to produce analyses by assimilating observations every hour with 10-km horizontal grid spacing and a 60-member multiphysics ensemble. Four assimilation experiments are conducted using different sets of observations: CONV, consisting of conventional observations from NCEP’s prepBUFR files; AWS, combining CONV and dense automatic surface weather station networks (AWS), SATWND, combining AWS with satellite-derived winds, and RAD, including SATWND; and satellite radiances from different microwave and infrared sensors. The assimilation of observations with high temporal and spatial frequency generates an important impact on the PBL, primarily on the precipitable water content, that leads to the development of deep convection and heavy precipitation closer to the observed in this case study. The assimilation of radiance observations produces a better development of the convection mainly during the mature state of the MCS leading to an increase in the accumulated precipitation. Ensemble forecasts initialized from each experiment were also simulated to evaluate their skill to predict precipitation. The hourly assimilation of the observations in AWS, SATWND, and RAD helped to improve the precipitation forecast.

Analysis of Microtopography Atmospheric Precipitable Water Vapour over the Northeastern Margin of the Qinghai–Tibet Plateau

The Liupan Mountain (LPM) area is located on the northeast margin of the Qinghai–Tibet Plateau, which is the western part of the second ladder of China’s terrain. It is also an intersection area of two air currents, which are caused by the combined action of the Qinghai–Tibet Plateau, the middle and lower levels of the westerly belt and the edge of the monsoon area. LPM is one of the main air water vapour transport pathways in Northwest China as well as a main water conservation area for nearly ten million people. Research on atmospheric precipitable water vapour (PWV) variation characteristics in LPM is beneficial for understanding the mechanisms of orographic precipitation and improving the effects of weather modification. Based on the data from 10 Global Navigation Satellite System Meteorology (GNSS/MET) stations for 6 years and the data of automatic weather stations in the LPM, the temporal and spatial variation characteristics of PWV in the LPM were analysed, and the differences in PWV in the 24 h before and after precipitation were compared in this study. The results showed that the hourly, monthly and seasonal variations in PWV displayed obvious patterns. PWV increased rapidly 10 h ahead of precipitation, while it decreased rapidly within 10 h after precipitation, which was slower than that before precipitation. In terms of spatial distribution, PWV was larger in the south than in the north and larger in the east than in the west. Although the precipitation on the LPM peak was the highest in the whole LPM area, its PWV was always the lowest, indicating that the PWV was obviously affected by the air temperature. This showed that under the same water vapour condition, precipitation was more likely to form in the area with low temperature, and the precipitation was larger, which also provided a train of thought for improving the method of artificial precipitation enhancement by using the condensation catalyst.

Effects of satellite LAI data on modelling land surface temperature and related energy budget in the Noah-MP land surface model

• Satellite LAI scheme can provide more accurate vegetation dynamic information. • Satellite LAI scheme improved LST simulation in Noah-MP land surface model. • Mechanism of LAI on LST simulation was revealed by energy budget analysis in model. Leaf area index (LAI) is essential for modelling the land surface temperature (LST) and related energy budget in the land surface models (LSMs). Due to the lack of a parameterization scheme based on satellite LAI data, it is unclear whether satellite LAI data can improve the performance of Noah with multi-parameterization (Noah-MP) LSM to model LST in China land data assimilation system (CLDAS). Here, by using satellite LAI data reconstructed by the advanced deep learning method, we established a new vegetation scheme based on satellite data for the Noah-MP LSM in the CLDAS. We quantitatively evaluated simulation effects of different vegetation schemes including the parameter table scheme (TAB), the dynamic vegetation scheme (DVEG), and the satellite LAI data scheme (OBS), on modelling LAI, LST and their related energy budget in China from 2016 to 2018. We found that except for the cropland, the LAI of the vegetation in growing season observed by satellite was lower than that simulated by the Noah-MP LSM. Validations based on the automatic weather stations in the vegetation-covered area showed that compared with the TAB and DVEG scheme, the OBS scheme improved the negative bias and high unbiased root mean square error (ubRMSE) of simulated LST. Compared with the TAB and DVEG scheme, the optimal improvements of the OBS scheme for annual mean bias and annual mean ubRMSE were 0.32 °C and 0.17 °C, respectively. We further revealed the mechanism of LAI on LST simulation in the Noah-MP LSM by decomposed temperature metric method. With the adoption of the new satellite LAI-based scheme, the absorbed radiation (SW), sensible heat flux (H), latent heat flux (LE), and ground heat flux (G) simulated by the Noah-MP LSM tend to change in different directions, leading to an overall increase in the net energy absorbed by the surface, which is consistent with the LST changes. The LAI changes of dense vegetation affect the LST simulation mainly through the competition of the absorbed shortwave radiation and turbulent heat fluxes. Due to the influence of exposed soil, the LAI changes of sparse vegetation mainly affect the LST simulation mainly through the interaction of the absorbed shortwave radiation and ground heat flux. Our study highlighted that the application of satellite LAI data has the potential to improve simulations of multiple land surface processes in the Noah-MP LSM.

Modeling cloud properties over the 79 N Glacier (Nioghalvfjerdsfjorden, NE Greenland) for an intense summer melt period in 2019

AbstractLong believed to be insignificant, melt activity on the Northeast Greenland Ice Stream (NEGIS) has increased in recent years. Summertime Arctic clouds have the potential to strongly affect surface melt processes by regulating the amount of radiation received at the surface. However, the cloud effect over Greenland is spatially and temporally variable and high‐resolution information on the northeast is absent. This study aims at exploring the potential of a high‐resolution configuration of the polar‐optimized Weather Research & Forecasting Model (PWRF) in simulating cloud properties in the area of the Nioghalvfjerdsfjorden Glacier (79 N Glacier). Subsequently, the model simulations are employed to investigate the impact of Arctic clouds on the surface energy budget and on surface melting during the extensive melt event at the end of July 2019. Compared to automatic weather station (AWS) measurements and remote‐sensing data (Sentinel‐2A and the Moderate Resolution Imaging Spectroradiometer, MODIS), PWRF simulates cloud properties with sufficient accuracy. It appears that peak melt was caused by an increase in solar radiation and sensible heat flux (SHF) in response to a blocking anticyclone and foehn winds in the absence of clouds. Cloud warming over high‐albedo surfaces helped to precondition the surface and prolonged the melting as the anticyclone abated. The results are sensitive to the surface albedo and suggest spatiotemporal differences in the cloud effect as snow and ice properties change over the course of the melting season. This demonstrates the importance of including high‐resolution information on clouds in analyses of ice sheet dynamics.

Air-to-water heat pumps’ seasonal performance for heating in 16 Greek locations with climate data of the last decade

Heat pumps (HPs) are attracting lately increasing attention due to their features to meet the sustainability criteria. Air-to-water heat pumps (AWHPs) have gained prominent success because they can be put in place of oil or gas boilers in heating systems. AWHPs energy consumption and their contribution to the reduction of heating systems gas emissions is strongly related to the seasonal coefficient of performance for heating (SCOP), which is affected by various factors with the most decisive to be the ambient temperature. The SCOP is crucial for the energy labelling of HPs and the estimation of operation costs in every location. The aim of this paper is to estimate and compare the SCOP of various AWHPs models installed in buildings at 16 different Greek locations, in the 4 climate zones of Greece, by using hourly temperature data of the last decade (2010-2019) which were recorded in a network of automatic weather stations across Greece. Numerical results show that the climate plays a key role in the calculation of the seasonal efficiency of AWHPs but also highlight the importance of using different temperature data for each location, even if they are in the same climate zone, in order to obtain accurate results.