European Centre For Medium-Range Weather Forecasts Data Research Articles

Correlation Analysis of Evapotranspiration, Emissivity Contrast and Water Deficit Indices: A Case Study in Four Eddy Covariance Sites in Italy with Different Environmental Habitats

Evapotranspiration (ET) represents one of the essential processes controlling the exchange of energy by terrestrial vegetation, providing a strong connection between energy and water fluxes. Different methodologies have been developed in order to measure it at different spatial scales, ranging from individual plants to an entire watershed. In the last few years, several methods and approaches based on remotely sensed data have been developed over different ecosystems for the estimation of ET. In the present work, we outline the correlation between ET measured at four eddy covariance (EC) sites in Italy (situated either in forest or in grassland ecosystems) and (1) the emissivity contrast index (ECI) based on emissivity data from thermal infrared spectral channels of the MODIS and ASTER satellite sensors (CAMEL data-set); (2) the water deficit index (WDI), defined as the difference between the surface and dew point temperature modeled by the ECMWF (European Centre for Medium-Range Weather Forecasts) data. The analysis covers a time-series of 1 to 7 years depending on the site. The results showed that both the ECI and WDI correlate to the ET calculated through EC. In the relationship WDI-ET, the coefficient of determination ranges, depending on the study area, between 0.5 and 0.9, whereas it ranges between 0.5 and 0.7 when ET was correlated to the ECI. The slope and the sign of the latter relationship is influenced by the vegetation habitat, the snow cover (particularly in winter months) and the environmental heterogeneity of the area (calculated in this study through the concept of the spectral variation hypothesis using Rao’s Q heterogeneity index).

Validation of the Aeolus L2B Rayleigh winds and ECMWF short‐range forecasts in the upper troposphere and lower stratosphere using Loon super pressure balloon observations

AbstractThe novel Aeolus satellite, which carries the first Doppler wind lidar providing profiles of horizontal line‐of‐sight (HLOS) winds, addresses a significant gap in direct wind observations in the global observing system. The gap is particularly critical in the tropical upper troposphere and lower stratosphere (UTLS). This article validates the Aeolus Rayleigh–clear wind product and short‐range forecasts of the European Centre for Medium‐Range Weather Forecasts (ECMWF) with highly accurate winds from the Loon super pressure balloon network at altitudes between 16 and 20 km. Data from 229 individual balloon flights are analysed, applying a collocation criterion of 2 hr and 200 km. The comparison of Aeolus and Loon data shows systematic and random errors of 0.31 and 6.37 ms, respectively, for the Aeolus Rayleigh–clear winds. The horizontal representativeness error of Aeolus HLOS winds (nearly the zonal wind component) in the UTLS ranges from 0.6–1.1 ms depending on the altitude. The comparison of Aeolus and Loon datasets against ECMWF model forecasts suggests that the model systematically underestimates the HLOS winds in the tropical UTLS by about 1 ms. While Aeolus winds are currently considered as point winds by the ECMWF data assimilation system, the results of the present study demonstrate the need for a more realistic HLOS wind observation operator for assimilating Aeolus winds.

A Joint Method for Wave and Wind Field Parameter Inversion Combining SAR with Wave Spectrometer Data

Synthetic aperture radar (SAR) and wave spectrometer are common methods for observing the ocean wind field and waves on the sea surface, but both have limitations. Due to the influence of velocity bunching modulation, SAR wave observation is limited by the azimuth cut-off phenomenon. Meanwhile, SAR relies on an external wind direction source, so it is difficult for SAR to observe wind fields independently. There is no azimuthal cut-off phenomenon when the spectrometer observes the sea surface, but its azimuth resolution is much lower than SAR. Combining the above characteristics, the joint inversion of SAR and wave spectrometer for sea-state parameters becomes possible. In this paper, a joint method for wind field and wave parameter inversion combining SAR with spectrometer data is proposed. In this method for wave parameter inversion, the wave spectrum of the spectrometer was used as a first-guess spectrum of SAR wave spectrum inversion, the fit wave spectrum obtained by joint inversion and the modified wave spectrum of the spectrometer were fused to form a new spectrum, and the wave parameters were calculated. For wind field parameter inversion, wind direction was obtained using a wave spectrometer, and was used as the input of the SAR wind field inversion. Wind speed was obtained using the CMOD5.N method. Collocated data from Sentinel-1 SAR, the wave spectrometer, the National Data Buoy Center (NDBC) buoy, and the European Centre for Medium-Range Weather Forecasting (ECMWF) are used to verify the proposed method. Sea parameters retrieved from the spectrometer and SAR are compared to the buoy and ECMWF data. The results show that the root mean square errors of significant wave height, mean wave period, wind direction and wind speed are 0.37 m, 1.02 s, 22.7° and 1.06 m/s with ECMWF data, and 0.35 m, 0.78 s, 18.22° and 0.92 m/s with buoy data, respectively. By comparing the inversion results with the L2 products of SAR and SWIM, it can be concluded that the inversion accuracy of the joint method is higher in the middle and low sea conditions. Therefore, the joint inversion method for wind field and wave parameters proposed in this paper has good results, which verifies the accuracy of the joint inversion method.

Diagnostic Analysis of Multimodel Rainstorm Forecast for Cases Based on MODE Method

It is very important to analyze the performance of the model rainstorm forecast for understanding the intensity and position deviation of the model precipitation and improving the forecast ability. This paper uses classical scoring and the MODE (Method for Object-Based Diagnostic Evaluation) method to evaluate the forecast performance of different products. The forecast and observation data used in the article mainly include CMA (China Meteorological Administration) multi-source merged precipitation analysis, the precipitation forecast of ECMWF (European Centre for Medium-Range Weather Forecasts), CMA-Meso (Mesoscale model forecast of CMA) and SCMOC (National Meteorological Center grid precipitation forecast guidance product) data. At the same time, the possible correction method of heavy rainfall area is explored by using the high and low-level circulation configuration of the ECMWF model. The main conclusions are as follows: ① MODE spatial verification shows that the number, intensity, area and location of ECMWF rainstorm precipitation objects match the observed precipitation best, which is obviously better than SCMOC and CMA-Meso precipitation forecasts. There are significantly fewer SCMOC rainstorm precipitation objects, and the area of each single precipitation object is significantly larger, which often fails to report the small area objects of convective precipitation. ② The reason for the high TS score of SCMOC is that it reduces the number of small area rainstorm objects and avoids the “double punishment” phenomenon caused by position forecast error, which leads to the failure of SCMOC in local rainstorm forecasts. ③ Analyzing the relationship between the circulation situation of the ECMWF model and the location of rainstorm forecasts by the model, it is found that the location of the rainstorm area is consistent with the upper circulation system, especially with the strong rising area of the vertical velocity of 700 hPa and the high value area of the specific humidity of 850 hPa. When the rainstorm area coincides with the upper air system but is not consistent with the ground convergence area and the high value area of velocity potential, the rainstorm location often has a large deviation. The location of the surface convergence area can be used as a reference to improve the performance of the rainstorm forecast.

Function-Based Troposphere Tomography Technique for Optimal Downscaling of Precipitation

Precipitation is an important meteorological indicator that has a direct and significant impact on ecology, agriculture, hydrology, and other vital areas of human health and life. It is therefore essential to monitor variations of this parameter at a global and local scale. To monitor and predict long-term changes in climate elements, Global Circulation Models (GCMs) can provide simulated global-scale climatic processes. Due to the low spatial resolution of these models, downscaling methods are required to convert such large-scale information to regional-scale data for local applications. Among the downscaling methods, the Statistical DownScaling Model (SDSM) and the Artificial Neural Networks (ANNs) are widely used due to their low computational volume and suitable output. These models mainly require training data, and generally, the reanalysis data obtained from the National Center for Environmental Prediction (NCEP) and European Centre for Medium-range Weather Forecasts (ECMWF) are used for this purpose. With an optimal downscaling method, instead of applying the humidity indices extracted from ECMWF data, the outputs of the function-based tropospheric tomography technique obtained from the Global Navigation Satellite System (GNSS) will be used. The reconstructed function-based tropospheric data is then fed to the SDSM and ANN methods used for downscaling. The results of both methods indicate that the tomography can increase the accuracy of the downscaling process by about 20 mm in the wet months of the year. This corresponds to an average improvement of 38% with regard to the root mean square error (RMSE) of the monthly precipitation.

ECMWF short-term prediction accuracy improvement by deep learning

This paper aims to describe and evaluate the proposed calibration model based on a neural network for post-processing of two essential meteorological parameters, namely near-surface air temperature (2 m) and 24 h accumulated precipitation. The main idea behind this work is to improve short-term (up to 3 days) forecasts delivered by a global numerical weather prediction (NWP) model called ECMWF (European Centre for Medium-Range Weather Forecasts). In comparison to the existing local weather models that typically provide weather forecasts for limited geographic areas (e.g., within one country but they are more accurate), ECMWF offers a prediction of the weather phenomena across the world. Another significant benefit of this global NWP model includes the fact, that by using it in several well-known online applications, forecasts are freely available while local models outputs are often paid. Our proposed ECMWF-enhancing model uses a combination of raw ECMWF data and additional input parameters we have identified as useful for ECMWF error estimation and its subsequent correction. The ground truth data used for the training phase of our model consists of real observations from weather stations located in 10 cities across two European countries. The results obtained from cross-validation indicate that our parametric model outperforms the accuracy of a standard ECMWF prediction and gets closer to the forecast precision of the local NWP models.

Vertical Atmospheric Structures Associated with Positive Biases in COSMIC-2 Refractivity Retrievals

Abstract Representation of complex vertical structures observed in the troposphere can vary depending on data sources. The radio occultation (RO) technique offers great advantages for sensing the atmosphere down to its lowermost layers using high-resolution measurements collected by satellites on low-Earth orbit (LEO). The structures are generally smoother in vertical when reproduced from atmospheric models. We evaluate the quality of troposphere retrievals from the COSMIC-2 mission and demonstrate that systematic effects in fractional refractivity deviations with respect to European Centre for Medium-Range Weather Forecasts (ECMWF) background fields are spatially correlated with positive refractivity gradients characterized as subrefraction. The magnitude of refractivity biases observed mostly over the equatorial regions can exceed 1% within altitudes of 3–5 km. Respective zonal means reveal seasonal trends linked with the distribution of atmospheric inversion layers and signal-to-noise ratio values in RO data. The positive biases are vertically collocated with significant refractivity gradients in COSMIC-2 retrievals that are not reflected in the corresponding ECMWF profiles. The analysis of gradients based on COSMIC-2 data, further supported by radiosonde observations, suggests that most of subrefractions is identified in the middle troposphere at around 4 km. While the altitudes of maximum refractivity gradients from COSMIC-2 and ECMWF data are in fairly good agreement, the magnitude of ECMWF gradients is significantly smaller and rarely exceeds positive values.

Development of a New Tropical Cyclone Strip Segment Retrieval Model for C-Band Cross-Polarized SAR Data

Compared with co-polarized (HH/VV) normalized radar cross-section (NRCS) backscattered from the sea surface, there is no saturation phenomenon in cross-polarized (HV/VH) NRCS when wind speed is greater than about 20 m/s, so cross-polarized synthetic aperture radar (SAR) images can be used for high wind speed monitoring. In this work, a new geophysical model function (GMF) is proposed to describe the relation of the C-band cross-polarized NRCS with wind speed and radar incidence angle. Here, sixteen ScanSAR wide mode SAR images acquired by RADARSAT-2 (RS-2) under tropical cyclone (TC) conditions and the matching wind speed data from the European Centre for Medium-Range Weather Forecasts (ECMWF) and the Stepped-Frequency Microwave Radiometer (SFMR) are collected and divided into datasets A and B. Dataset A is used for analyzing the effects of the wind field and radar incidence angle on the reference noise-removed cross-polarized NRCS, and for proposing the new GMF for each sub-swath of the SAR images, while dataset B is used to retrieve wind speed and evaluate the validity of the new GMF. The comparisons between the wind speeds retrieved by the new GMF and the collocated ECMWF and SFMR data demonstrate the excellent performance of the new GMF for wind speed retrieval. To analyze the universality of the new GMF, wind speed retrievals based on 32 Sentinel-1A/B (S-1A/B) extra-wide-swath (EW) mode images acquired under TC conditions are also compared with the collocated wind speeds measured by the Soil Moisture Active Passive (SMAP) radiometer, and the retrieved wind speeds have RMSE of 3.667 m/s and a bias of 2.767 m/s. The successful applications in high wind speed retrieval of different tropical cyclones again supports the availability of the new GMF.

Study of the Effect of an Environmentally Friendly Flood Risk Reduction Approach on the Oman Coastlines during the Gonu Tropical Cyclone (Case Study: The Coastline of Sur)

Tropical cyclones may be destructive in the coastal region, such as the Gonu tropical cyclone, which affected the Arabian Peninsula and parts of southern Iran in 2007. In this study, a coupled MIKE 21/3 HD/SW (hydrodynamic/spectral wave) model was used to simulate the inland flooding inside the Sur port during the Gonu tropical cyclone. The MIKE 21 Cyclone Wind Generation (CWG) tool was utilized to generate the cyclone’s wind and pressure field. The required input data were obtained from the International Best Track Archive for Climate Stewardship (IBTrACS) and imported into the CWG tool. In this study, the wind and pressure fields were compared between the analytical vortex model and European Centre for Medium-Range Weather Forecasts (ECMWF) data during the Gonu cyclone passage. Moreover, by developing a new model, artificial Mangroves’ effect on inland flooding was investigated. The results show that, contrary to the ECMWF data, the analytical vortex models well captured the storm event’s wind and pressure field. Furthermore, the flood hazard is calculated based on the inundation depth, flow velocity, and area’s vulnerability. The flood hazard map shows that 5% of the coast is at high-risk, 49% is at medium-risk, and 46% is at low-risk class in the Sur port. By applying Mangroves as flood risk reduction, the high-risk area is almost completely removed. However, medium and low-risk zones increase by 50% and 50%, respectively. This information could be helpful in disaster risk reduction and coastal management in the future.

Koreksi Bias Statistik Pada Data Prediksi Suhu Permukaan Air Laut Di Wilayah Indian Ocean Dipole Barat Dan Timur

The IOD can be measured using the Dipole Mode Index (DMI) which is calculated based on the sea surface temperature in the Indian Ocean. Therefore, DMI can be predicted using sea surface temperature forecasting data, such as data provided by the European Center for Medium-Range Weather Forecasts (ECMWF). However, the data still has a bias as compared to the actual data, so to get a more accurate prediction, corrected data is needed. Therefore, the aim of this study is to predict DMI based on sea surface temperature forecasting data that has been corrected for bias using the quantile mapping method, a method that connects the distribution of forecasting and actual data. The results showed that the DMI prediction using corrected data was more accurate than the DMI prediction using ECMWF data. DMI predictions using corrected data have high accuracy to predict IOD events in October-April.

Flex_extract v7.1.2 – a software package to retrieve and prepare ECMWF data for use in FLEXPART

Abstract. Flex_extract is an open-source software package to efficiently retrieve and prepare meteorological data from the European Centre for Medium-Range Weather Forecasts (ECMWF) as input for the widely used Lagrangian particle dispersion model FLEXPART and the related trajectory model FLEXTRA. ECMWF provides a variety of data sets which differ in a number of parameters (available fields, spatial and temporal resolution, forecast start times, level types etc.). Therefore, the selection of the right data for a specific application and the settings needed to obtain them are not trivial. Consequently, the data sets which can be retrieved through flex_extract by both member-state users and public users as well as their properties are explained. Flex_extract 7.1.2 is a substantially revised version with completely restructured code, mainly written in Python 3, which is introduced with all its input and output files and an explanation of the four application modes. Software dependencies and the methods for calculating the native vertical velocity η˙, the handling of flux data and the preparation of the final FLEXPART input files are documented. Considerations for applications give guidance with respect to the selection of data sets, caveats related to the land–sea mask and orography, etc. Formal software quality-assurance methods have been applied to flex_extract. A set of unit and regression tests as well as code metric data are also supplied. A short description of the installation and usage of flex_extract is provided in the Appendix. The paper points also to an online documentation which will be kept up to date with respect to future versions.

Analysis of the convective available potential energy by precipitation over Iraq using ECMWF data for the period of 1989–2018

The Convective Available Potential Energy (CAPE) represents the amount of energy for a sample of air. The sample departs vertically within the atmosphere and through these values the potential energy to predict the extreme weather conditions such as storms, hurricanes, lightning and thunder. Data are taken by CAPE, convective precipitation (Cp) and total precipitation (Tp) from satellites recorded by the European Centre for Medium-Range Weather Forecasts (ECMWF). The choice of 30 years (1989–2018) over Iraq station between two latitudes (29.5°–37.22° N) and two longitudes (48.45°–38.45° E). Otherwise, we have studied total yearly mean of CAPE, Cp and Tp over Iraq, the total monthly mean of CAPE, Cp and Tp for the selected station, as well as the relationship between of CAPE, Cp and Tp for the selected station. The results showed that the highest total yearly mean of CAPE, Cp and Tp over Iraq was included northern stations and lowest was included central and southern stations. The highest total monthly mean of CAPE, Cp and Tp for Zakho station. The relationship between the CAPE and Cp is positive and the relationship between CAPE and Tp is positive too at five stations but Mosul station represents very high correlation while Zakho station represents the low correlation.

An improved GNSS tropospheric tomography method with the GPT2w model

The GNSS tropospheric tomography technique has been proven to be a powerful tool for three-dimensional water vapor reconstruction. In most previous studies, the signals leaving the side face of the tomography area are ignored as having invalid information, which wastes valuable observations and decreases signal coverage of the research area. To include the contribution of such signals to the final tomographic result, an improved tropospheric tomography approach, which makes the most use of GNSS observations by combining the data derived from the empirical Global Pressure and Temperature 2 wet model, is proposed. Compared to the conventional method, the proposed method can adaptively use the signals penetrating from the model’s side face to the tomographic model, which increases the number of voxels crossed by rays and improves the stability of the tomography model. Numerical results in Hong Kong over the period of day of year 124–150, 2013 show that the internal accuracy of the tomographic model based on the proposed method increases by 9.8% when compared to the conventional method. The RMS errors of the integrated water vapor derived from the proposed method are 4.1 and 4.6 mm, respectively, while the values derived from the conventional method are 5.0 and 5.4 mm, respectively, when compared to the radiosonde and European Centre for Medium-Range Weather Forecasts (ECMWF) products. In addition, compared to the conventional method, the accuracy of the water vapor density profile derived from the tomographic result of the proposed method has been enhanced by 25% and 12.5% when the radiosonde and ECMWF data are considered as the reference, respectively. Such results indicate a good performance of the proposed method for GNSS troposphere tomography.

An improved weighted mean temperature (Tm) model based on GPT2w with Tm lapse rate

Global pressure and temperature 2 wet (GPT2w) is an empirical model providing the mean values plus annual and semiannual amplitudes of weighted mean temperature (Tm), which makes it a widely used tool in converting zenith wet delay (ZWD) to precipitable water vapor (PWV) in GNSS meteorology. The model meets the needs of real-time Tm anywhere in the world without relying on any other meteorological observations compared with traditional Tm calculation methods. It outperforms the other empirical Tm models released in recent years. Due to the lack of the Tm vertical adjustment in the model, the accuracy of Tm estimated by the model is subject to certain constraints, especially at sites which have large altitude differences compared with the GPT2w grid points. We explored the Tm lapse rate for the vertical adjustment using 10 years of 37 monthly mean pressure level data from the European Center for Medium-Range Weather Forecasts (ECMWF) and extended the GPT2w model to a new one called the GPT2wh model. Three schemes with different height ranges were established to fit the Tm lapse rate, and the most appropriate scheme was selected by adopting the goodness of fit measures, including the coefficient of determination (R-squared) and the root mean square error (RMSE). In addition to the mean value, annual and semiannual amplitudes for Tm lapse rate on a regular 1° grid were determined and stored in the GPT2wh model. The performance of the new model was assessed against the GPT2w model using different data sources in 2011, i.e., the ECMWF data and globally distributed radiosonde data. The numerical results show that the GPT2wh model outperforms the GPT2w model with an improved RMSE of 7.36/5.00/2.45/1.37/0.51/0.03 K at different height levels in the ECMWF comparison. In comparison with the radiosonde data, the mean RMSE of the GPT2wh model improves by 0.33 K from 4.16 to 3.83 K, i.e., an approximately 8% improvement against the GPT2w model. The impact of Tm on GNSS-PWV was analyzed, showing that the GPT2wh model can effectively improve the accuracy of the converted PWV.

Water Vapor in the Asian Summer Monsoon Anticyclone: Comparison of Balloon‐Borne Measurements and ECMWF Data

AbstractWater vapor (H2O) is the strongest greenhouse gas in our atmosphere. Hence, accurate measurements and a correct representation in global models of H2O in the upper troposphere/lower stratosphere (UTLS) are important for understanding and projecting climate. Here we compare balloon‐borne measurements of UTLS H2O, performed by cryogenic frostpoint hygrometers (CFH) and meteorological radiosondes (Vaisala RS41) during two intensive field campaigns in the Asian summer monsoon anticyclone region, with humidity data from three products of the European Centre for Medium‐range Weather Forecasts (ECMWF): operational analysis and forecast (termed OPERA), ERA‐Interim reanalysis, and the newly released ERA5 reanalysis. Taking CFH as a reference, we show that OPERA and ERA5 provide a more accurate representation of UTLS H2O than ERA‐Interim. In particular, OPERA and ERA5 similarly overestimate H2O mixing ratios by on average 0.7–0.8 ppmv (14–15%) and 0.7–0.9 ppmv (15–17%) at pressures 60–100 hPa, respectively, and both provide a good representation of the observed vertical distribution (including fine structures) and natural variability of UTLS H2O. In contrast, ERA‐Interim underestimates UTLS H2O by 0.6–1.7 ppmv (14–30%), and it fails to capture relevant features of the vertical distribution of UTLS H2O. At pressures (p) lower than 60 hPa, all three ECMWF products are in good agreement with CFH. Humidity measurements by RS41 show an average dry bias of 0.1–0.5 ppmv (3–9%) compared to CFH for 60–100 hPa, and a moist bias increasing with altitude for p < 60 hPa, exceeding 100% for p < 40 hPa.

An improved pixel-based water vapor tomography model

Abstract. As an innovative use of Global Navigation Satellite System (GNSS), the GNSS water vapor tomography technique shows great potential in monitoring three-dimensional water vapor variation. Most of the previous studies employ the pixel-based method, i.e., dividing the troposphere space into finite voxels and considering water vapor in each voxel as constant. However, this method cannot reflect the variations in voxels and breaks the continuity of the troposphere. Moreover, in the pixel-based method, each voxel needs a parameter to represent the water vapor density, which means that huge numbers of parameters are needed to represent the water vapor field when the interested area is large and/or the expected resolution is high. In order to overcome the abovementioned problems, in this study, we propose an improved pixel-based water vapor tomography model, which uses layered optimal polynomial functions obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) by adaptive training for water vapor retrieval. Tomography experiments were carried out using the GNSS data collected from the Hong Kong Satellite Positioning Reference Station Network (SatRef) from 25 March to 25 April 2014 under different scenarios. The tomographic results are compared to the ECMWF data and validated by the radiosonde. Results show that the new model outperforms the traditional one by reducing the root-mean-square error (RMSE), and this improvement is more pronounced, at 5.88 % in voxels without the penetration of GNSS rays. The improved model also has advantages in more convenient expression.

GNSS-derived PWV and comparison with radiosonde and ECMWF ERA-Interim data over mainland China

Precipitable water vapour (PWV) is a key factor for monitoring climate and the hydrological cycle. Here, PWV is calculated using 249 ground-based GNSS stations derived from the Crustal Movement Observation Network of China (CMONOC). Zenith total delay (ZTD) is estimated using the GAMIT/GLOBK (Ver. 10.4) and the zenith hydrostatic delay (ZHD) is calculated using the layered European Centre for Medium-Range Weather Forecasting (ECMWF) ERA-Interim data. The GNSS-derived ZTD is validated using the radiosonde and ECMWF data with the root mean square errors (RMSE) of 19.1 mm and 12.5 mm, respectively. The ECMWF-derived surface pressure (Ps) used to calculate the ZHD and the weighted mean temperature (Tm) calculated based on the layered ECMWF data are also evaluated using radiosonde data with RMSE values of 1.14 hPa and 1.24 K, respectively. Consequently, the final CMONOC-derived PWV at 249 stations is obtained and compared with radiosonde-derived and ECMWF-derived PWV while the RMSE values are 1.38 mm and 1.30 mm, respectively. The two-dimensional (2-d) PWV image derived from CMONOC is also compared with that from ECMWF, which shows good consistency across different seasons.

Estimation of Coffee Yield from Gridded Weather Data

Core Ideas Use of gridded weather data [European Center for Medium‐Range Weather Forecasts (ECMWF) and NASA] gave similar estimations of coffee yield to the models that use surface‐observed weather data. A new calibration was proposed to the model to the penalties and the coefficients calibrated from the different data sources. The calibration of the coefficients for the ECMWF and NASA data was efficient. The tests with independent data confirmed the improvement of the calibration. Coffee yield can be estimated using the ECMWF and NASA grid data. Agrometeorological models have been applied to crop yield estimations, providing information for management. A limiting factor of these models is the input data, which come mostly from surface meteorological stations (SMS). We propose the use of gridded weather data provided by the European Center for Medium‐Range Weather Forecasts (ECMWF) and the NASA as a viable and innovative alternative for coffee (Coffea arabica L.) yield estimation in Brazil. We made modifications in the coefficients of the model proposed by Santos and Camargo, regarding the penalties for extreme temperatures and those related to different gridded data sources. The accuracy, measured by the mean absolute percentage error (MAPE), was 23.76, 24.61, and 22% for the calibrations with ECMWF, NASA, and SMS data, respectively. These high levels of MAPE were the result of the high biennality of the crop. The average tendency, measured by the systematic root mean square error, was an overestimation (or underestimation) of ±465 kg ha−1 by the ECMWF, ±411 kg ha−1 by NASA, and ±653 by the SMS models. The mean precision, measured by the nonsystematic root mean square error, was 186, 190, and 280 kg ha−1 for the ECMWF, NASA, and SMS models, respectively. These results indicate that coffee yield for São Paulo and Minas Gerais can be calibrated and estimated with the ECMWF and NASA gridded data.

Estimation of multipath propagation and fade margin over Coastal area

Atmospheric weather parameter is dynamic in nature, hence the need for continuous investigation of the anomalous propagation phenomenon. The coastal region is more unique in its characteristics due to the rapid and continuous variation in the weather parameters. This paper presents the results from 10 years (2006 – 2015) of reanalysis data of meteorological parameters (temperature, relative humidity and atmospheric pressure) obtained from European Center for Medium-Range Weather Forecasts (ECMWF). The data covers some selected stations in the coastal region of Nigeria namely: Port-Harcourt, Warri, Calabar, Arogbo, Oron, Yenagoa and Lagos Island at four synopsies hours of the day (6 hrs, 12 hrs, 18 hrs and 24 hrs). The resolution of the ECMWF data is 0.25 by 0.25. Radio refractivity, refractivity gradient, point refractivity and geoclimatic factor are estimated from the data. Subsequently, the results were used to deduce percentage of fade depth exceedance. The overall resultsa will assist to ascertain the level of signal degradation due to multipath fading and fade depth over the coastal regions of Nigeria

Extending a model for water vapor sounding by ground-based GNSS in the vertical direction

Weighted mean temperature (Tm) is a crucial parameter for retrieving precipitable water vapor (PWV) in Global Navigation Satellite System (GNSS) meteorology. The difference between the height of the GNSS receiver and the height of the derived Tm may cause large uncertainty in the value of the retrieved PWV. In this study, we analyze the vertical variation of Tm on a global scale and build a new vertical correction model using the operational reanalysis of the European Centre for Medium-Range Weather Forecasts (ECMWF). Another dataset from ECMWF and the radiosonde data derived from 678 globally distributed stations are used to independently validate the performance of the newly built model and demonstrate its superiority compared with the existing vertical correction method. The results show that the lapse rate of Tm has geographic and seasonal variations, and the Tm has large nonlinear variation along the vertical direction in the high-latitude regions, especially in the polar regions. A performance improvement of 15%–35% compared with the existing vertical correction method is achievable for the newly built model when tested with the ECMWF data, and the improvement is 10%–25% when tested with the radiosonde data.