Need for mountain weather stations climbs

In central Italy, particularly in the Umbria-Marche Apennines, there are some complete, high-altitude weather stations, which are very important for assessing the climate in these areas. The mountain weather stations considered in this study were Monte Bove Sud (1917 m.a.s.l.), Monte Prata (1816 m.a.s.l.) and Pintura di Bolognola (1360 m.a.s.l.). The aim of this research was to compare the differences between the precipitation measured by the rain gauges and the data obtained by satellite using the IMERG algorithm, at the same locations. The evaluation of possible errors in the estimation of precipitation using one method or the other is fundamental for obtaining a reliable estimate of precipitation in mountain environments. The results revealed a strong underestimation of precipitation for the rain gauges at higher altitudes (Monte Bove Sud and Monte Prata) compared to the same pixel sampled by satellite. On the other hand, at lower altitudes, there was a better correlation between the rain gauge value and the IMERG product value. This research, although localised in well-defined locations, could help to assess the problems in rain detection through mountain weather stations.

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Deterministic Ensemble Forecasts Using Gene-Expression Programming*

Weather and Forecasting ◽

10.1175/2009waf2222192.1 ◽

2009 ◽

Vol 24 (5) ◽

pp. 1431-1451 ◽

Cited By ~ 32

Author(s):

Atoossa Bakhshaii ◽

Roland Stull

Keyword(s):

Gene Expression ◽

Fitness Function ◽

Gene Expression Programming ◽

Ensemble Forecast ◽

Ensemble Average ◽

Limited Area ◽

Ensemble Forecasts ◽

Weather Stations ◽

Mountain Weather ◽

Horizontal Grid

Abstract A method called gene-expression programming (GEP), which uses symbolic regression to form a nonlinear combination of ensemble NWP forecasts, is introduced. From a population of competing and evolving algorithms (each of which can create a different combination of NWP ensemble members), GEP uses computational natural selection to find the algorithm that maximizes a weather verification fitness function. The resulting best algorithm yields a deterministic ensemble forecast (DEF) that could serve as an alternative to the traditional ensemble average. Motivated by the difficulty in forecasting montane precipitation, the ability of GEP to produce bias-corrected short-range 24-h-accumulated precipitation DEFs is tested at 24 weather stations in mountainous southwestern Canada. As input to GEP are 11 limited-area ensemble members from three different NWP models at four horizontal grid spacings. The data consist of 198 quality controlled observation–forecast date pairs during the two fall–spring rainy seasons of October 2003–March 2005. Comparing the verification scores of GEP DEF versus an equally weighted ensemble-average DEF, the GEP DEFs were found to be better for about half of the mountain weather stations tested, while ensemble-average DEFs were better for the remaining stations. Regarding the multimodel multigrid-size “ensemble space” spanned by the ensemble members, a sparse sampling of this space with several carefully chosen ensemble members is found to create a DEF that is almost as good as a DEF using the full 11-member ensemble. The best GEP algorithms are nonunique and irreproducible, yet give consistent results that can be used to good advantage at selected weather stations.

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Calculation of Potential Evapotranspiration and Calibration of the Hargreaves Equation Using Geostatistical Methods over the Last 10 Years in Central Italy

Geosciences ◽

10.3390/geosciences11080348 ◽

2021 ◽

Vol 11 (8) ◽

pp. 348

Author(s):

Matteo Gentilucci ◽

Margherita Bufalini ◽

Marco Materazzi ◽

Maurizio Barbieri ◽

Domenico Aringoli ◽

...

Keyword(s):

Agricultural Sector ◽

Potential Evapotranspiration ◽

High Reliability ◽

Central Italy ◽

Accurate Estimate ◽

Geostatistical Methods ◽

Budget Analysis ◽

Mountain Environment ◽

Weather Stations ◽

Mountain Weather

Potential evapotranspiration (ET0) is an indicator of great interest for water budget analysis and the agricultural sector. Therefore, the purpose of this study was to make the calculation reliable even if only the temperature data were present. In this research, the ET0 was initially calculated for a limited number of weather stations (12) using the Penman–Monteith method. In some cases, the simplified Penman–Monteith formula was adopted, while in others, as in the case of mountain weather stations, the complete formula was employed to consider the differences in vegetation, deduced from satellite surveys. Subsequently, the ET0 was calculated with the Hargreaves–Samani (HS) formula, calibrating the Hargreaves coefficient, through the spatialization of ET0, by the geostatistical method. The results showed a high reliability of the HS method in comparison with simplified PM (PM) method, and complete Penman–Monteith (cPM) method, with a minimum calibration of the empirical Hargreaves coefficient. In particular, a very good correlation between the results obtained in the mountain environment with the uncalibrated HS method and the cPM method was also observed in this area, while PM showed discordant and much higher results than ET0 compared with the other methods. It follows that this procedure allowed a more accurate estimate of potential evapotranspiration with a view to territory management, both in terms of water resources and the irrigation needs of the vegetation.

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Mountain Weather and Climate Third Edition

10.1017/cbo9780511754753 ◽

2008 ◽

Cited By ~ 258

Author(s):

Roger G. Barry

Keyword(s):

Weather And Climate ◽

Mountain Weather

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Stochastic simulation of the spatio-temporal field of the average daily heat index in Southern Russia

Climate Research ◽

10.3354/cr01623 ◽

2020 ◽

Vol 82 ◽

pp. 149-160

Author(s):

N Kargapolova

Keyword(s):

Heat Waves ◽

Numerical Models ◽

Heat Index ◽

Real Field ◽

Gaussian Field ◽

The Real ◽

Southern Russia ◽

Weather Stations ◽

Spatio Temporal ◽

Temporal Field

Numerical models of the heat index time series and spatio-temporal fields can be used for a variety of purposes, from the study of the dynamics of heat waves to projections of the influence of future climate on humans. To conduct these studies one must have efficient numerical models that successfully reproduce key features of the real weather processes. In this study, 2 numerical stochastic models of the spatio-temporal non-Gaussian field of the average daily heat index (ADHI) are considered. The field is simulated on an irregular grid determined by the location of weather stations. The first model is based on the method of the inverse distribution function. The second model is constructed using the normalization method. Real data collected at weather stations located in southern Russia are used to both determine the input parameters and to verify the proposed models. It is shown that the first model reproduces the properties of the real field of the ADHI more precisely compared to the second one, but the numerical implementation of the first model is significantly more time consuming. In the future, it is intended to transform the models presented to a numerical model of the conditional spatio-temporal field of the ADHI defined on a dense spatio-temporal grid and to use the model constructed for the stochastic forecasting of the heat index.

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Coram Experimental Forest daily meteorology data for Desert Ridge and Terrace Hills weather stations: 1996-2015

Forest Service Research Data Archive ◽

10.2737/rds-2017-0003 ◽

2017 ◽

Author(s):

Lance S. Glasgow ◽

David K. Wright ◽

Elaine K. Sutherland

Keyword(s):

Weather Stations

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Climate variations and change evident in high-quality climate data for Australia's Antarctic and remote island weather stations

Australian Meteorological and Oceanographic Journal ◽

10.22499/2.6204.005 ◽

2013 ◽

Vol 62 (4) ◽

pp. 247-261 ◽

Cited By ~ 11

Author(s):

B Jovanovic ◽

K Braganza ◽

D Collins ◽

D Jones

Keyword(s):

Climate Variations ◽

Climate Data ◽

High Quality ◽

Remote Island ◽

Weather Stations

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Evaluation of NASA POWER Reanalysis Products to Estimate Daily Weather Variables in a Hot Summer Mediterranean Climate

Agronomy ◽

10.3390/agronomy11061207 ◽

2021 ◽

Vol 11 (6) ◽

pp. 1207

Author(s):

Gonçalo C. Rodrigues ◽

Ricardo P. Braga

Keyword(s):

Wind Speed ◽

Bias Correction ◽

Goodness Of Fit ◽

Mediterranean Climate ◽

Weather Data ◽

Coefficient Of Determination ◽

Mean Bias Error ◽

Bias Error ◽

Weather Variables ◽

Weather Stations

This study aims to evaluate NASA POWER reanalysis products for daily surface maximum (Tmax) and minimum (Tmin) temperatures, solar radiation (Rs), relative humidity (RH) and wind speed (Ws) when compared with observed data from 14 distributed weather stations across Alentejo Region, Southern Portugal, with a hot summer Mediterranean climate. Results showed that there is good agreement between NASA POWER reanalysis and observed data for all parameters, except for wind speed, with coefficient of determination (R2) higher than 0.82, with normalized root mean square error (NRMSE) varying, from 8 to 20%, and a normalized mean bias error (NMBE) ranging from –9 to 26%, for those variables. Based on these results, and in order to improve the accuracy of the NASA POWER dataset, two bias corrections were performed to all weather variables: one for the Alentejo Region as a whole; another, for each location individually. Results improved significantly, especially when a local bias correction is performed, with Tmax and Tmin presenting an improvement of the mean NRMSE of 6.6 °C (from 8.0 °C) and 16.1 °C (from 20.5 °C), respectively, while a mean NMBE decreased from 10.65 to 0.2%. Rs results also show a very high goodness of fit with a mean NRMSE of 11.2% and mean NMBE equal to 0.1%. Additionally, bias corrected RH data performed acceptably with an NRMSE lower than 12.1% and an NMBE below 2.1%. However, even when a bias correction is performed, Ws lacks the performance showed by the remaining weather variables, with an NRMSE never lower than 19.6%. Results show that NASA POWER can be useful for the generation of weather data sets where ground weather stations data is of missing or unavailable.

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