European Centre For Medium-Range Weather Forecasts Research Articles

Using CloudSat to Advance the Global Precipitation Climatology Project (GPCP) over Antarctica

Remote sensing-based precipitation products face several challenges in high latitudes and specifically over frozen surfaces (i.e., snow and ice). Consequently, precipitation estimates tend to be lower in quality over these regions, including Antarctica, the coldest continent on Earth. In this study, we developed a method for adjusting precipitation estimates over Antarctica by leveraging CloudSat's ability to capture snowfall compared to other satellite products over snow and ice surfaces. We addressed limitations of CloudSat, such as poor spatiotemporal sampling, noise, and incomplete coverage near the poles. We utilized the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5th Generation (ERA5) to guide development, particularly during the period when the TIROS Operational Vertical Sounder (TOVS) was contributing. We adjusted monthly and seasonal TOVS and Atmospheric Infrared Sounder – Infrared (AIRS-IR) precipitation biases over Antarctica at the pixel level, as these two products are the primary inputs for the Global Precipitation Climatology Project (GPCP) in high latitudes. We assessed the adjusted TOVS and AIRS-IR through analyses of geographical maps and time series of monthly and seasonal mean precipitation rates. The results are encouraging, indicating that the proposed approach could replace the current approach used in the GPCP for adjusting precipitation estimates from AIRS-IR and TOVS over Antarctica. Adjusting precipitation estimates from TOVS and AIRS-IR using the proposed approach improves the Kling-Gupta efficiency (KGE) over their entire period by 162% and 147%, respectively. Moreover, the proposed approach can be applied to adjust other precipitation products over Antarctica at the pixel level, including satellites and reanalysis products.

Climate classification for Northeast Brazil using reanalysis data and the Absolute Aridity Index

The climate of the Northeast Region of Brazil (NEB) has been intensively studied and analyzed for climate classification. The aridity index of the United Nations Environment Programme (UNEP) (AIUNEP) has been used for this purpose, but without fully satisfactory results. The input variables needed for its calculation are precipitation and reference potential evapotranspiration (ET0). However, although rainfall stations recording routine measurements of precipitation are well distributed in the NEB, they do not provide the necessary variables for estimating ET0. Thus, interpolation is used to calculate ET0, but this can generate errors. Another objective climate classification approach is the Thornthwaite method, based on the determination of the moisture index (Im), whose calculation also requires weather station data. Thus, seeking to circumvent the problem of paucity of stations and improve the spatial distribution of information on meteorological variables in the NEB, the present work had as one of its objectives to validate reanalysis data from ERA5 of the European Center for Medium-range Weather Forecast (ECMWF) and the unified gauge-based analysis of global daily precipitation project of the Climate Prediction Center/National Oceanic and Atmospheric Administration (CPC/NOAA). After validation, climate classifications were developed for the NEB using the AIUNEP and Im. It was observed that the Thornthwaite climate classification overestimated the aridity in the NEB, while the IAUNEP tended to underestimate it. For this reason, a new climate classification index, called the absolute aridity index (Iab), was suggested, yielding satisfactory results.

Evaluation of precipitation extremes in ERA5 reanalysis driven regional climate simulations over the CORDEX-Australasia domain

Reanalysis-driven regional climate simulations using the Weather Research and Forecasting (WRF) model in New South Wales (NSW) and Australian Regional Climate Modelling (NARCliM) Version 2.0 are assessed for capturing precipitation extreme indices. Seven configurations of the WRF model driven by ECMWF (European Centre for Medium-Range Weather Forecasts) Reanalysis v5 (ERA5) for Australia from 1979 to 2020 at 20 km resolution are evaluated. We assess the spatiotemporal patterns of six selected Expert Team on Sector-Specific Climate Indices (ET-SCI) precipitation extremes by comparing regional climate model (RCM) simulations against gridded observations. The RCMs evaluated have varying levels of accuracy in simulating precipitation extremes. While they capture climatology and coefficient of variation of precipitation extremes relatively well, temporal correlation and trend reproduction present challenges. Some RCMs perform more effectively for specific extreme indices, while others encounter challenges in accurately replicating them. No single RCM excels in all aspects, highlighting the need to consider specific strengths when selecting RCMs for global climate model (GCM) driven simulations.

Assimilation of GNSS tropospheric gradients into the Weather Research and Forecasting (WRF) model version 4.4.1

Abstract. In this study, we have incorporated tropospheric gradient observations from a Global Navigation Satellite Systems (GNSS) ground station network into the Weather Research and Forecasting (WRF) model through a newly developed observation operator. The experiments aim at testing the functionality of the developed observation operator and at analyzing the impact of tropospheric gradients on the sophisticated data assimilation (DA) system. The model was configured for a 0.1° mesh over Germany with 50 vertical levels up to 50 hPa. Our initial conditions were obtained from the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) data at 0.25° resolution, and conventional observations were obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF), restricted to mainly surface stations and radiosondes. We selected approximately 100 GNSS stations with high data quality and availability covering Germany. We performed DA every 6 h for June and July 2021. Four experiments were conducted: (1) a control run assimilating only conventional observations; (2) an impact run assimilating zenith total delays (ZTDs) on top of the control run; (3) an impact gradient run assimilating ZTDs and gradients on top of the control run; and (4) a gradient run assimilating only gradients on top of the control run. The error for the impact run was reduced by 32 % and 10 % for ZTDs and gradients, whereas the error for the impact gradient run was reduced by 35 % and 18 %, respectively. The gradient errors for the gradient run were nearly equal to those of the impact gradient. Overall, the newly developed operator for the WRFDA system works as intended. In particular, the combined assimilation of gradients and the ZTDs led to a notable improvement in the humidity field at altitudes above 2.5 km. With the operator codes developed and freely available to the WRF users, we aim to trigger further GNSS tropospheric gradient assimilation studies.

Investigation of retrieval method for aerosol and water vapor profiles based on MAX-DOAS technology

Simultaneously obtaining the vertical distribution and profile of aerosol and water vapor is crucial for in-depth understanding of the Earth's radiation balance, regional water cycle, and the causes of frequent haze weather in China. This study developed a system and inversion method based on multi-axis differential optical absorption spectroscopy (MAX-DOAS) to simultaneously retrieve tropospheric aerosol and water vapor profiles. The absorption of dioxy (O4) was obtained by multi-elevation measurement using the developed ground-based MAX-DOAS system. By minimizing a cost function combining an atmospheric radiative transfer model, aerosol extinction profiles were first inverted, followed by water vapor profiles. Optimized parameter selection in the look-up table method reduced reliance on prior profile information, enhancing pollutant distribution retrieval accuracy. In the monitoring period in Huaibei region, aerosols were mainly below 1.5 km, while water vapor concentrations decreased with altitude. Comparisons of water vapor vertical column densities (VCDs) retrieved through the look-up table method with the European Centre for Medium-Range Weather Forecasts(ECMWF) ERA5 model and geometric approximation showed good agreement, with correlation coefficients of 0.93 and 0.98, respectively. To comprehend water vapor sources at various vertical layers, a 24-hour backward trajectory clustering analysis was conducted using the HYSPLIT model based on observed wind fields. Findings revealed that at 500 meters altitude, water vapor primarily emanated from the southeast, whereas at 1 km and 2 km altitudes, it predominantly originated from the southwest. The study advances simultaneous tropospheric aerosol and water vapor profile retrieval, providing reliable technical support for the investigation of regional pollution.

How well can global ensemble forecasts predict tropical cyclones in the southwest Indian Ocean?

AbstractThe southwest Indian Ocean (SWIO) recently experienced its most active, costliest and deadliest cyclone season on record (2018–2019). The anticipation and forecasting of natural hazards, such as tropical cyclones, are crucial to preparing for their impacts, but it is important to understand how well forecasting systems can predict them. Despite the vulnerability of the SWIO to tropical cyclones, comparatively little research has focused on this region, including understanding the ability of numerical weather prediction systems to predict cyclones and their impacts in southeast Africa. In this study, we evaluate ensemble probabilistic and high‐resolution deterministic forecasts of tropical cyclones in the SWIO from 2010 to 2020, using two state‐of‐the‐art global forecasting systems: one from the European Centre for Medium‐Range Weather Forecasts (ECMWF) and the other from the U.K. Met Office. We evaluate predictions of the track, assessing the location of the centre of each storm and its speed of movement, as well as its intensity, looking at maximum wind speeds and minimum central pressure, and discuss how the forecasts have evolved over the 10‐year period. Overall, ECMWF typically provides more accurate forecasts, but both systems tend to underestimate translation speed and intensity. We also investigate the impact of the Madden‐Julian Oscillation (MJO) on tropical cyclones and their forecasts. The MJO impacts where and when tropical cyclones form, their tracks and intensities, which in turn impacts forecast skill. These results are intended to provide an increased understanding of the ability of global forecasting systems to predict tropical cyclones in the SWIO, for the purpose of decision making and anticipatory action.

Deep Learning of a 200-member Ensemble with a Limited Historical Training to Improve the Prediction of Extreme Precipitation Events

Abstract This study introduces a deep learning (DL) scheme to generate reliable and skillful probabilistic quantitative precipitation forecasts (PQPFs) in a postprocessing framework. Enhanced machine learning model architecture and training mechanisms are proposed to improve reliability and skill of PQPFs while permitting computationally efficient model fitting using a short training dataset. The methodology is applied to postprocessing of 24h accumulated PQPFs from an ensemble forecast system recently introduced by the Center for Western Weather and Water Extremes (CW3E) and for lead times from one to six days. The ensemble system was designed based on a high-resolution version of the Weather Research and Forecasting (WRF) model, named West-WRF, to produce a 200-member ensemble in near real-time (NRT) over the Western U.S. during the boreal cool seasons to support forecastinformed reservoir operations (FIRO) and studies of prediction of heavy-to-extreme events. Postprocessed PQPFs are compared with those from the raw West-WRF ensemble, the operational Global Ensemble Forecast System (GEFSv12) as well as the ensemble from the European Centre for Medium-Range Weather Forecasts (ECMWF). As an additional baseline, we provide PQPFs verification metrics from a recently developed neural network postprocessing scheme proposed by Ghazvinian et al. (2022). Results demonstrate that skill of postprocessed forecasts significantly outperform PQPFs and deterministic forecasts from raw ensembles and the recently developed algorithm. The resulting PQPFs broadly improve upon the reliability and skill of baselines in predicting heavy to extreme precipitation (e.g., > 75 mm) across all lead times while maintaining the spatial structure of the high-resolution raw ensemble.

Climate Variability of Air Temperature and Its Warming Trends in the Arabian Gulf

AbstractOver a 41-year period (1979–2019), the climate of the Arabian Gulf (Gulf) exhibited notable variability. This variation, influenced by mesoscale and globalscale meteorological and climatic determinants, showed temperature fluctuations, predominantly in the Northern Gulf (N Gulf). The N Gulf recorded an average temperature of 25.9 °C, a noticeable 3 °C higher than the broader Gulf average. This period marked extreme temperature records, with a peak at 53.8 °C in July 2016, paralleling the highest known temperatures in Asia. Data reveals a warming trend (0.1–0.6 °C per decade) in the region, with the N Gulf’s rise surpassing global trends. The years 2014–2019 were notably warmer, impacting overall trends, and 1997 was the transitional year shifting from cooler to warmer climates. The N Gulf’s seasonal dynamics are also transforming, with extended summers and shortened winters. In this study, we employed empirical orthogonal function (EOF) decomposition analysis to examine the interannual to multidecadal fluctuations in air temperature within the Gulf from 1979 to 2019, using hourly air temperature data from the Watch Forcing Dataset (WFDE5) generated from the fifth generation (ERA5) European Centre for Medium-Range Weather Fore-casts (ECMWF). Through this method, we discerned three principal EOF modes, which together accounted for 82% of the air temperature variability in the Gulf, highlighting external influences such as North Atlantic Oscillation (NAO), El Nino-Southern Oscillation (ENSO), and Atlantic Multidecadal Oscillation (AMO). The data for these external influences were sourced from the National Oceanic and Atmospheric Administration (NOAA) Physical Sciences Laboratory. These findings stress the pressing need for detailed climate studies and mitigation strategies for a rapidly warming Gulf region, especially the N Gulf.

Use of CAMS near Real-Time Aerosols in the HARMONIE-AROME NWP Model

Near real-time aerosol fields from the Copernicus Atmospheric Monitoring Services (CAMS), operated by the European Centre for Medium-Range Weather Forecasts (ECMWF), are configured for use in the HARMONIE-AROME Numerical Weather Prediction model. Aerosol mass mixing ratios from CAMS are introduced in the model through the first guess and lateral boundary conditions and are advected by the model dynamics. The cloud droplet number concentration is obtained from the aerosol fields and used by the microphysics and radiation schemes in the model. The results show an improvement in radiation, especially during desert dust events (differences of nearly 100 W/m2 are obtained). There is also a change in precipitation patterns, with an increase in precipitation, mainly during heavy precipitation events. A reduction in spurious fog is also found. In addition, the use of the CAMS near real-time aerosols results in an improvement in global shortwave radiation forecasts when the clouds are thick due to an improved estimation of the cloud droplet number concentration.

Opposite effects of aerosols and meteorological parameters on warm clouds in two contrasting regions over eastern China

Abstract. The sensitivity (S) of cloud parameters to the influence of different aerosol and meteorological parameters has in most previous aerosol–cloud interaction (aci) studies been addressed using traditional statistical methods. In the current study, relationships between cloud droplet effective radius (CER) and aerosol optical depth (AOD, used as a proxy for cloud condensation nuclei, CCN), i.e., the sensitivity (S) of CER to AOD, are investigated with different constraints of AOD and cloud liquid water path (LWP). In addition to traditional statistical methods, the geographical detector method (GDM) is applied in this study to quantify the relative importance of the effects of aerosol and meteorological parameters, as well as their interaction, on S. Moderate Resolution Imaging Spectroradiometer (MODIS) C6 L3 data and European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-5 reanalysis data, for the period from 2008 to 2022, were used to investigate aci over eastern China. Two contrasting areas were selected: the heavily polluted Yangtze River Delta (YRD) and a relatively clean area over the East China Sea (ECS). Linear regression analysis shows that CER decreases with the increase in AOD (negative S) in the moderately polluted atmosphere (0.1<AOD<0.3) over the ECS, whereas, in contrast, CER increases with increasing AOD (positive S) in the polluted atmosphere (AOD>0.3) over the YRD. Evaluation of S as function of the LWP shows that in the moderately polluted atmosphere over the ECS, S is negative in the LWP interval [40 g m−2, 200 g m−2], and the sensitivity of CER to AOD is substantially stronger as LWP is larger. In contrast, in the polluted atmosphere over the YRD, S is positive in the LWP interval [0 g m−2, 120 g m−2] and does not change notably as function of LWP in this interval. The study further shows that over the ECS, the CER is larger for higher low tropospheric stability (LTS) and relative humidity (RH) but lower for higher pressure vertical velocity (PVV). Over the YRD, there is no significant influence of LTS on the relationship between CER and AOD. The GDM has been used as an independent method to analyze the sensitivity of cloud parameters to AOD and meteorological parameters (RH, LTS and PVV). The GDM has also been used to analyze the effects of interactions between two parameters and thus obtain information on confounding meteorological effects on the aci. Over the ECS, cloud parameters are sensitive to almost all parameters considered except for cloud top pressure (CTP), and the sensitivity to AOD is larger than that to any of the meteorological factors. Among the meteorological factors, the cloud parameters are most sensitive to PVV and least sensitive to RH. Over the YRD, the explanatory power of the sensitivity of cloud parameters to AOD and meteorological parameters is much smaller than over the ECS, except for RH, which has a statistically significant influence on CTP and can explain 74 % of the variation of CTP. The results from the GDM analysis show that cloud parameters are more sensitive to the combination of aerosol and a meteorological parameter than to each parameter alone, but confounding effects due to co-variation of both parameters cannot be excluded.

A review of the BuFeng-1 GNSS-R mission: calibration and validation results of sea surface and land surface

ABSTRACT In this paper, we will conclude the results of Bufeng-1 (BF-1) A/B data processing, calibration workflow, and validation of the calibrated sea surface winds, land surface soil moisture, and sea surface height measurements. Since 2019, the BF-1 mission has operated in-orbit for over 4 years. The Earth reflected delay Doppler maps (DDMs) are continuously collected to perform global sea surface and land observations. At the same time, the intermediate frequency (IF) raw data are also obtained for 12 seconds every pass in diagnostic mode. To begin with, a brief description of the spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) technique will be provided in the introduction. Next, we will present the overview of Chinese BF-1 mission and the data specifications used in our research. In the next section, the BF-1 mission-related spaceborne power calibration and validation are presented to show the support to power DDM observable production for sea surface and land surface applications. Then, the status of Chinese Beidou System (BDS) Equivalent Isotropic Radiated Power (EIRP) acquisition programme is then introduced. Furthermore, the latest sea surface height (SSH) measurements results including two modes (group delay and carrier phase) and wind speed derivation based on machine learning (ML) method will be spatial-temporal aligned and validated with auxiliary datasets including Denmark Technology University (DTU) mean sea surface (MSS) products and European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis. The previous published results of sea surface winds retrieval under Hurricane conditions and soil moisture retrieval are also reviewed for the BF-1 mission applications. Finally, the conclusion of BF-1 derived results will be discussed to draw out ongoing/future works.

Summer snowline altitude gradients in Western Norway are influenced by maritime climate

ABSTRACT Mountain snowline dynamics are relatively underreported with few studies exploring spatial snowline dynamics. Whilst clear regional-scale relationships between snowline location and temperature exist in European mountains, recent research at higher latitudes reports no response to climate change. In maritime mountains, snowlines occupy complex environmental gradients. Using timeseries of satellite data from Landsat missions 5–8 (151 images between 1984 and 2021), we explored sub-regional summer snowline dynamics across the maritime-continental climate gradient in the Western Norwegian mountains. We characterize spatio-temporal snowline altitude dynamics and investigate the climate factors altering snowline patterns. Summer snowline altitudes were found to increase inland at around double the rate of the 0°C summer isotherm. Data from the European Centre for Medium-Range Weather Forecasts (ECMWF) land component of the fifth generation of European Reanalysis (ERA5-Land), showed a potential ‘maritime-mountain’ effect with coastal orographic snowfall and cloud cover-induced surface solar downwelling radiation amplifying maritime-continental snowline altitude gradients alongside surface atmospheric temperature. This was replicated in the Canadian Rocky Mountains. Between 1984 and 2021, we found spatial summer snowline gradients in Norway decreased and propose multiple climate forcings are responsible, potentially masking links between snowlines and climate change. Although non-significant, the data also suggest regional summer snowline altitudes increased. This study demonstrates the complex spatial heterogeneity in snow-climate relationships and highlights how long-term snow dynamics can be queried using fine-grain (Landsat) resolution satellite data. We share our approach through a Google Earth Engine web-app that rapidly executes spatial snowline analyses for global mountain regions via a graphical user interface.

Local identification of equatorial Kelvin waves in real‐time operational forecasts

AbstractEquatorial Kelvin waves (KWs) are associated with a variety of equatorial atmospheric phenomena, namely, tropical convection, cloud and precipitation variability, tropical cyclogenesis, the onset of monsoon season over Africa and India, and even the quasi‐biennial oscillation and the Madden–Julian oscillation. As such, if operational forecasts are to improve near the Tropics, it is important that they correctly represent KWs. This article aims at developing a novel methodology for identifying KWs in real‐time operational forecasts at specific longitudes using only the meridional structures of the solutions to the free Laplace tidal equations, known as Hough vector functions. The main advantages of this newly proposed methodology are that (a) it can identify KWs at a specific longitude and (b) very low computational cost is needed to apply it to real‐time operational forecasts. The application of the method to 2015–2017 European Centre for Medium‐range Weather Forecasts (ECMWF) analysis and operational forecast data reveals a systematic bias in the representation of KWs by the ECMWF operational forecast model.

Improving the statistical downscaling performance of climatic parameters with convolutional neural networks

Abstract This study examines two downscaling techniques, convolutional neural networks (CNNs) and feedforward neural networks for predicting precipitation and temperature, alongside statistical downscaling model as a benchmark model. The daily climate predictors were extracted from the European Center for Medium-range Weather Forecast (ECMWF) ERA5 dataset spanning from 1979 to 2010 for Tabriz city, located in the northwest of Iran. The biases in precipitation data of ERA5 predictors were corrected through the empirical quantile mapping method. Also, two nonlinear predictor screening methods, random forest and mutual information were employed, alongside linear correlation coefficient. While these methods facilitate identification of dominant regional climate change drivers, it is essential to consider their limitations, such as sensitivity to parameter settings, assumptions about data relationships, potential biases in handling redundancy and correlation, challenges in generalizability across datasets, and computational complexity. Evaluation results indicated that CNN, when applied without predictor screening, achieves coefficient of determination of 0.98 for temperature and 0.71 for precipitation. Ultimately, future projections were employed under two shared socioeconomic pathways (SSPs), SSP2-4.5 and SSP5-8.5, and concluded that the most increase in temperature by 2.9 °C and decrease in precipitation by 3.5 mm may occur under SSP5-8.5.

Evaluation of VIIRS Thermal Emissive Bands Long-Term Calibration Stability and Inter-Sensor Consistency Using Radiative Transfer Modeling

This study investigates the long-term stability of the Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) moderate-resolution Thermal Emissive Bands (M TEBs; M12–M16) covering a period from February 2012 to August 2020. It also assesses inter-sensor consistency of the VIIRS M TEBs among three satellites (S-NPP, NOAA-20, and NOAA-21) over eight months spanning from 18 March to 30 November 2023. The field of interest is limited to the ocean surface between 60°S and 60°N, specifically under clear-sky conditions. Taking radiative transfer modeling (RTM) as the transfer reference, we employed the Community Radiative Transfer Model (CRTM) to simulate VIIRS TEB brightness temperature (BTs), incorporating European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis data as inputs. Our results reveal two key findings. Firstly, the reprocessed S-NPP VIIRS TEBs exhibit a robust long-term stability, as demonstrated through analyses of the observation minus background BT differences (O-B ∆BTs) between VIIRS measurements (O) and CRTM simulations (B). The drifts of the O-B BT differences are consistently less than 0.102 K/Decade across all S-NPP VIIRS M TEB bands. Notably, observations from VIIRS M14 and M16 stand out with drifts well within 0.04 K/Decade, reinforcing their exceptional reliability for climate change studies. Secondly, excellent inter-sensor consistency among these three VIIRS instruments is confirmed through the double-difference analysis method (O-O). This method relies on the O-B BT differences obtained from daily VIIRS operational data. The mean inter-VIIRS O-O BT differences remain within 0.08 K for all M TEBs, except for M13. Even in the case of M13, the O-O BT differences between NOAA-21 and NOAA-20/S-NPP have values of 0.312 K and 0.234 K, respectively, which are comparable to the 0.2 K difference observed in overlapping TEBs between VIIRS and MODIS. These disparities are primarily attributed to the significant differences in the Spectral Response Function (SRF) of NOAA-21 compared to NOAA-20 and S-NPP. It is also found that the remnant scene temperature dependence of NOAA-21 versus NOAA-20/S-NPP M13 O-O BT difference after accounting for SRF difference is ~0.0033 K/K, an order of magnitude smaller than the corresponding rates in the direct BT comparisons between NOAA-21 and NOAA-20/S-NPP. Our study confirms the versatility and effectiveness of the RTM-based TEB quality evaluation method in assessing long-term sensor stability and inter-sensor consistency. The double-difference approach effectively mitigates uncertainties and biases inherent to CRTM simulations, establishing a robust mechanism for assessing inter-sensor consistency. Moreover, for M12 operating as a shortwave infrared channel, it is found that the daytime O-B BT differences of S-NPP M12 exhibit greater seasonal variability compared to the nighttime data, which can be attributed to the idea that M12 radiance is affected by the reflected solar radiation during the daytime. Furthermore, in this study, we’ve also characterized the spatial distributions of inter-VIIRS BT differences, identifying variations among VIIRS M TEBs, as well as spatial discrepancies between the daytime and nighttime data.

Spatial-temporal characteristics analysis of solar irradiance forecast errors in Europe and North America

Accurate photovoltaic (PV) power forecast is crucial to power systems. Solar irradiance forecast is the fundamentals of PV power forecast. Current researches merely focus on improving the forecast accuracy. There hasn't been comprehensive study on the spatial-temporal characteristics of solar irradiance forecast errors. Thus, this paper analyzed the error characteristics of solar irradiance forecast provided by European Centre for Medium-Range Weather Forecasts (ECMWF), one of the most accurate numerical weather prediction (NWP) products. ECMWF's forecasts were compared with ERA5 solar irradiance reanalysis data to reveal the error distribution characteristics. Four-year (2017–2020) data from the geographical region bounded by 63°N, −126°W, 21°S, and 36°E (covering most parts of Europe and North America) were studied. Experiments show that solar irradiance forecast errors peak at noon of the local time. Furthermore, correlations between solar irradiance forecast errors and other weather variables were also revealed. Experiments show that solar irradiance forecast errors have negative correlation with low cloud cover forecast errors and positive correlation with air temperature forecast errors. The possible reasons for the correlation relationship were also analyzed in detail. This paper systematically reveals the spatial-temporal characteristics of solar irradiance forecast errors and provides a useful guideline for solar PV system operation.

Characterization of some secondary radio-climatic factors for reliable radio wave propagation and link’s design over North western Nigeria

A good understanding of the behavior of the atmosphere is a key factor in radio wave propagation and link’s design. This study investigated the influence of refractivity gradient, effective earth radius factor (k-factor) and geo-climatic factor (K-factor) on radio communication over the cities of Kaduna, Katsina and Sokoto, in Northwestern parts of Nigeria. Forty-one year’s secondary data (1980–2020) of temperature, pressure and humidity at the surface 12, 100 and 250 m above ground level retrieved from European Centre for Medium-Range Weather Forecasts (ECMRWF) ERA-5 reanalysis were used for the study. The three secondary radio-climatic parameters under investigation were computed and analyzed using latest ITU-R (Rec.P.453-14 (08/2019)) Models. It was observed that secondary radio-climatic variables exhibit seasonal type dependence. The research also reveals that the secondary radio-climatic variables depend on location, with all the study locations having different values. The highest value of refractivity gradient obtained is about −75.06 (N-units/km) in the month of February in Sokoto while the least of about −162.65 (N-units/km) was recorded in Kaduna in September. The value of the refractivity gradient is higher during the dry season compared to the wet season months with Sokoto recording the highest values for more than half of the months, followed by Katsina. Kaduna recorded highest number of lowest values of refractivity gradient. For the geoclimatic factor, it was revealed that Kaduna has an average maximum geoclimatic factor of 14.0000E-5 in September and an average minimum of 9.6677E-05 in February. Furthermore, the obtained values of k factor values for the study areas are higher than the standard value of, 43 (1.33). The k-factor in Kaduna ranges from 1.69 to 2.16 with mean value of 1.85 for dry season while for the wet season it ranges from 2.66 to 4.12 with a mean value of 3.74; the overall mean value for Kaduna is 2.80. In Katsina, it ranges from 1.66 to 2.20 with mean value of 1.81 for dry season while for the wet season it ranges from 1.95 to 4.00 with a mean value of 3.27; the overall mean value is 2.54. In Sokoto it ranges from 1.60 to 2.30 with mean value of 1.78 for dry season while for the wet season it ranges from 1.72 to 3.45 with a mean value of 2.59; the overall mean value is 2.19. The interpretation of this is that, radio signal propagation in the Northwest region of Nigeria is super-refractive. The overall findings of this research are essential for estimating location-dependent fade margins, which are useful for the design of wireless communication links for the region.

Assessment of ERA5 derived zenith tropospheric delay data over East African region

The Global Navigation Satellite Systems (GNSS) provide valuable data for computing accurate and reliable Zenith Tropospheric Delay (ZTD) products used in GNSS precise positioning, climate, and meteorological studies. However, inconsistent GNSS data streaming in the East African region, caused by poor internet connectivity, equipment failure, and electrical power outages, results in data gaps that pose an impact on the applications of the ZTD products. To ensure continuous ZTD data availability, alternative approaches are necessary to fill the gaps. This paper evaluates the accuracy and feasibility of computing ZTD over East Africa using the European Centre for Medium-Range Weather Forecasts (ECMWF) 5th Re-Analysis (ERA5) model. The ERA5 ZTD is compared with ZTD data from 13 GNSS stations for the years 2013 to 2020 from the Nevada Geodetic Laboratory (NGL) products. Seven statistical evaluation metrics, including mean bias (MnB), root mean squared error (RMSE), Pearson correlation coefficient (R), coefficient of determination (R2), refined index of agreement (IA), Nash–Sutcliffe coefficient of efficiency (NSE), and Taylor skill score (TSS) were used to determine the agreement between ERA5 and GNSS tropospheric products. The results indicate that ERA5ZTD products match well with GNSS ZTD products, with an average MnB of −0.80 mm, RMSE of 8.94 mm, R of 0.976, R2 of 0.952, IA of 0.986, TSS of 0.975, and NSE of 0.945. The high accuracy and stability of ERA5 data in computing ZTD make it an excellent alternative source of ZTD data to augment GNSS ZTD products for positioning and meteorological applications in the East African region.

Assessment of Sensitivity and Validity Hydrodynamic Model in Cisadane Using Delft3d Flow Model

The National Capital Integrated Coastal Development (NCICD) program was prioritized as a disaster reduction technology within the annual target of BRIN in 2022. It is a part of the North Java Coastal Integrated Development Program project. A coastal reservoir development plan at the Cisadane River estuary is one of the outcomes. In this case, hydrodynamic modelling is needed to acquire hydrodynamic properties under the existing condition of Cisadane downstream, and it applied the Delft3D-FLOW model. The bathymetric data used in this simulation was obtained from the assimilation of national bathymetric data with a grid spacing of 180 m and field survey data with a horizontal resolution of 10 m and transect spacing of 200 m seaward of the Cisadane estuary. It has interpolated in a regular structured grid with 100 m and 50 m grid spacing. In addition, other data used are three hourly wind data downloaded from the European Centre for Medium-Range Weather Forecasts (ECMWF) and the daily discharge of the Cisadane River in 2021. Astronomical water level data generated from TPXO 7.2 and TPXO 8 were used in the boundary conditions. These data are simulated using the two and three-dimensional flow model Delft3D-FLOW during November 1 – 15, 2021, using four manning coefficients (n) values, which are 0.03; 0.035; 0.04; and 0.045. The validation formulation used Normalization Root Mean Square Error (NRMSE), resulting in 5.3 - 9.6% for all the manning values and domains. The highest correlation coefficient of current velocity (u-v components) is acquired in the 10 km2 domain with 0.797 for the L3 (bottom layer).

Measurement of Downwelling Radiance Using a Low-Cost Compact Fourier-Transform Infrared System for Monitoring Atmospheric Conditions

Temperature and water vapor play crucial roles in the Earth’s climate system, and it is important to understand and monitor the variation in the thermodynamic profile within the lower troposphere. Among various observation platforms for understanding the vertical structure of temperature and humidity, ground-based Fourier-transform infrared (FTIR) can provide detailed information about the lower troposphere by complementing the limitations of radiosonde or satellite methods. However, these ground-based systems have limitations in terms of cost, operation, and mobility. Herein, we introduce a cost-effective and easily deployable FTIR observation system designed to enhance monitoring capabilities for atmospheric conditions. The atmospheric downwelling radiance spectrum of sky is measured by applying a real-time radiative calibration using a blackbody. From the observed radiance spectrum, the thermodynamic profile (temperature and the water vapor mixing ratio) of the lower troposphere was retrieved using an algorithm based on the optimal estimation method (OEM). The retrieved vertical structure results in the lower troposphere were similar to the fifth-generation reanalysis database (ERA-5) of the European Center for Medium-range Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction final analysis (NCEP FNL). This provides a potential possibility for monitoring atmospheric conditions by a compact FTIR system.