Satellite Application Facility Research Articles

Homogeneity Analysis of the CM SAF Surface Solar Irradiance Dataset Derived from Geostationary Satellite Observations

A satellite-based climate record of monthly mean surface solar irradiance (SIS) is investigated with regard to possible inhomogeneities in time. The data record is provided by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Climate Monitoring (CM SAF) for the period of 1983 to 2005, covering a disk area between ±70° in latitude and longitude. The Standard Normal Homogeneity Test (SNHT) and two other homogeneity tests are applied with and without the use of reference SIS data (from the Baseline Surface Radiation Network (BSRN) and from the ECMWF (European Centre for Medium-Range Weather Forecasts) ERA -Interim reanalysis. The focus is on the detection of break-like inhomogeneities, which may occur due to satellite or SIS retrieval algorithm changes. In comparison with the few suitable BSRN SIS observation series with limited extension in time (no data before 1992), the CM SAF SIS time series do not show significant inhomogeneities, even though slight discrepancies in the surface measurements appear. The investigation of the full CM SAF SIS domain reveal inhomogeneities related to most of the documented satellite and retrieval changes, but only for relatively small domain fractions (especially in mountainous desert-like areas in Africa). In these regions the retrieval algorithm is not capable of adjusting for the changes of the satellite instruments. For other areas, e.g., Europe, no such breaks in the time series are found. We conclude that the CM SAF SIS data record has to be further assessed and regionally homogenized before climate trend investigations can be conducted.

Simple models to compute solar global irradiance from the CMSAF product Cloud Fractional Coverage

Simple models are proposed to compute solar global irradiance by using the hourly Cloud Fractional Coverage (CFC) data provided by the Climate Monitoring Satellite Application Facility (CMSAF). The models are tested against measurements performed in five Romanian weather stations. The cloudy sky models based on CFC (n, for short) are compared with cloudy sky models based on ground-based estimates of point cloudiness (C, for short). Two models were proposed here for clear sky and overcast sky defined as n = 0 and n = 1, respectively. Two types of cloudy sky regression models were built on the basis of these clear sky and overcast sky models. Eight cloudy sky models based on n have been tested in a particular location. The bias error is good or good enough for all cloudiness classes. The spreading error is good for n = 0 ÷ 0.3; good enough for n = 0.3 ÷ 0.7 and poor for n > 0.7. For low zenith angle (Z = 0° ÷ 30°) the bias error of the eight models is generally good enough or poor. Generally, best fit models based on C perform better than best fit models based on n. One model (D1) has been selected for further testing. The sub-model D1TOT has been obtained by fitting the model D1 to all available measured data. The accuracy of sub-model D1TOT is good and good enough for all stations at low and intermediate zenith angles (Z < 70°). The performance of a model based on n is significantly better than that of a model based on C, for all zenith angle classes. D1 sub-models were developed by using data from particular stations. Generally, all sub-models have good or good enough performance when used in stations other than the origin one, for cloudiness classes n < 0.7. In case of skies with n = 0.3 ÷ 0.7, the performance of the sub-models based on n is obviously worse than that of sub-models based on C. For low zenith angles (Z = 0° ÷ 70°), the performance of D1 sub-models is good or good enough, when applied in the origin station or other stations, and it is comparable with that of models based on C.

On the uncertainties in validating satellite instantaneous rainfall estimates with raingauge operational network

In the last decade, satellite precipitation estimation techniques have reached significant improvement in quantitative description of rainfall intensity and distribution, and even higher performances are expected from the full exploitation of the Global Precipitation Measurement Mission. Parallel to the development of new techniques the need of accurate and reliable ground reference fields is also growing, for both calibrating and validating satellite algorithms.In the frame of the EUMETSAT's Satellite Application Facility for the Support to Operational Hydrology and Water Management (H-SAF) different satellite estimation techniques are developed with the aim to provide instantaneous to 24h cumulated products to assist hydrological implementations, supported by a careful validation activity. Following the EUMETSAT guidelines, raingauges (and/or radar) have to be used as reference for satellite technique validation. The different satellite and raingauge views of the precipitation field, however, pose several problems when they have to be compared: several factors cumulate with each other throughout the matching process, resulting in large discrepancies between the two rainfall fields. In this work, we evaluate the impact of two of these factors in the satellite estimation validation process, taking advantage of the availability of locally-dense, high-resolution (1min) raingauge data over Italy. We first estimate the error due to the different time and spatial sampling between raingauges and satellite products (often referred to as representativeness error), and then we evaluate the impact of raingauge density variations on the interpolated maps obtained by different techniques.Results show that a careful selection of the raingauge network density, time sampling, and raingauge interpolation algorithms can greatly reduce such matching errors that, however, can be very high. A Fractional Standard Error ranging from 100% to 300% between satellite estimate and raingauge reference value can be due to different raingauge sampling and interpolation strategy, while a reduction in the raingauge density of a factor of 2 leads to a degradation of the quality of the interpolation of about 40%.

A surface radiation climatology across two Meteosat satellite generations

Long-term observations of the surface radiation budget are essential for climate monitoring, climate model evaluation and solar energy applications. The Satellite Application Facility on Climate Monitoring (CM SAF) released a climate data record (CDR) of global and direct surface irradiance as well as effective cloud albedo derived from observations of the Meteosat First Generation satellites (MFG, 1983–2005). This study presents an extension of this CDR using measurements from the Meteosat Second Generation satellites (MSG, 2004–present). This extended surface radiation dataset spans nearly 30years of data and, therefore, is in its uniquely high temporal and spatial resolution a valuable contribution to the climate community.In order to enable climatological consistency and homogeneity, the retrieval algorithm had to be modified for MSG: 1. The two narrowband visible channels of the MSG satellites are combined to simulate the MFG broadband visible channel; 2. The maximum cloud reflectance is empirically adjusted to account for the differences in the dynamic range of MSG compared to MFG.The extended dataset is tested for homogeneity and no significant breaks are detected during the overlap period of 2004–2005. Validation of the extended global radiation dataset against ground based observations from the Baseline Surface Radiation Network yields a mean monthly absolute bias of 8.15Wm−2. This complies with the target accuracy threshold of 15Wm−2 defined by the Global Climate Observing System.Global radiation has an overall positive, and significant, trend over the Meteosat disk which is mainly due to a negative trend in the effective cloud albedo, i.e., a decrease in cloudiness. Trends due to changes in the clear sky radiation are small and only induced by trends in the water vapor fields. Trends caused by changes in the direct effects of atmospheric aerosol are not represented because an aerosol climatology is used.

A comparison of data sources for creating a long-term time series of daily gridded solar radiation for Europe

Satellite-derived surface solar radiation estimates are an alternative to the solar radiation measured at weather stations or modelled from other measured meteorological variables. The advantage of satellite-derived solar radiation is its high spatial and temporal resolution in comparison with solar radiation derived from weather stations, which has to be spatially interpolated. Solar radiation estimates at approximately 3–5km resolution derived from geostationary Meteosat satellites are available for Europe through the EUMETSAT Satellite Application Facilities (SAFs). The SAF responsible for land monitoring (LSA-SAF) has been providing daily solar radiation estimates in near real-time since 2005. The SAF on climate monitoring (CM-SAF) provided a 23-year long (1983–2005) consistent dataset of daily solar radiation. In this study we examine if these two solar radiation datasets may effectively be merged to generate a long-term gridded solar radiation time series for Europe. Further, we evaluate whether the ERA-Interim reanalysis or interpolated measured solar radiation (JRC-MARS) can be used as a replacement for existing and possible future data gaps in the satellite-based dataset. We show that the root mean square error and mean absolute error of LSA-SAF’s and the CM-SAF’s solar radiation estimates are similar (p<0.05), calculated against measured solar radiation data. A grid-based comparison of LSA-SAF’s and CM-SAF’s datasets showed an average root mean square difference over Europe of 2MJm−2 and a mean difference of 0.37MJm−2. For replacing data gaps in satellite-based radiation, we recommend the use of the ERA-Interim reanalysis data; they correspond better to both the ground reference and satellite-derived solar radiation data as compared to interpolated JRC-MARS. We conclude that both satellite-based products can be concatenated to create long-term gridded time series of solar radiation for Europe.

The EUMETSAT OSI SAF near 50 GHz sea ice emissivity model

ABSTRACTA sea ice thermal microwave emission model for 50 GHz was developed under EUMETSAT's Ocean and Sea Ice Satellite Application Facility (OSI SAF) programme. The model is based on correlations between the surface brightness temperature at 18, 36 and 50 GHz. The model coefficients are estimated using simulated data from a combined thermodynamic and emission model. The intention with the model is to provide a first guess sea ice surface emissivity estimate for atmospheric temperature sounding applications in the troposphere in numerical weather prediction (NWP) models assimilating Advanced Microwave Sounding Unit (AMSU) and Special Sensor Microwave Imager/Sounder (SSMIS) data. The spectral gradient ratio is defined as the difference over the sum of the SSMIS brightness temperatures at 18 and 36 GHz vertical linear polarisation (GR1836). The GR1836 is related to the emissivity at the atmospheric temperature sounding channels at around 50 GHz. Furthermore, the brightness temperatures and the polarisation ratio (PR) at the neighbouring 18, 36 and 50 GHz channels are highly correlated. Both the gradient ratio at 18 and 36 GHz and the PR at 36 GHz measured by SSMIS are input into the model predicting the 50 GHz emissivity for horizontal and vertical linear polarisations and incidence angles between 0° and 60° The simulated emissivity is compared to the emissivity derived with alternative methods. The fit to real AMSU observations is investigated using the different emissivity estimates for simulating the observations with atmospheric data from a regional weather prediction model.

Definition and impact of a quality index for radar-based reference measurements in the H-SAF precipitation product validation

Abstract. The EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF) provides rainfall estimations based on infrared and microwave satellite sensors on board polar and geostationary satellites. The validation of these satellite estimations is performed by the H-SAF Precipitation Product Validation Group (PPVG). A common validation methodology has been defined inside the PPVG in order to make validation results from several institutes comparable and understandable. The validation of the PR-OBS-3 (blended infrared–microwave (IR–MW) instantaneous rainfall estimation) product using radar-based rainfall estimations as ground reference is described herein. A network of C-band and Ka-band radars throughout Europe ensures a wide area coverage with different orographic configurations and climatological regimes, but the definition of a quality control protocol for obtaining consistent ground precipitation fields across several countries is required. Among the hydro-meteorological community, the evaluation of the data quality is a quite consolidated practice, even though a unique definition of a common evaluation methodology between different countries and institutions has not been set up yet. Inside H-SAF, the first definition of the quality index of the radar rainfall observations has been introduced at the Italian Civil Protection Department (DPC). In the evaluation of the DPC quality index, several parameters are considered, some measured by the radar itself (static clutter map, range distance, radial velocity, texture of differential reflectivity, texture of co-polar correlation coefficient and texture of differential phase shift) and some obtained by external sources (digital elevation model, freezing layer height). In some cases, corrections were applied for clutter and beam blocking. The DPC quality index was calculated and applied to some relevant meteorological events reported by a radar test site in Italy. The precipitation field derived by radar data was compared with the PR-OBS-3 precipitation product, with varying thresholds of quality index: the impact of the introduction of the quality index defined on the statistical results of the satellite product validation as well as their sensitivity to the threshold choice were thus evaluated. Results show that PR-RMSE (a relative RMSE here introduced) is reduced from values between 2.5 and 3 to values around 1 when the quality threshold is increased from 0 (no threshold) to 0.8. Fractional standard error also decreases, from values around 2 to values around 1.5 in the same span of the quality threshold.

A Satellite-Based Surface Radiation Climatology Derived by Combining Climate Data Records and Near-Real-Time Data

This study presents a method for adjusting long-term climate data records (CDRs) for the integrated use with near-real-time data using the example of surface incoming solar irradiance (SIS). Recently, a 23-year long (1983–2005) continuous SIS CDR has been generated based on the visible channel (0.45–1 μm) of the MVIRI radiometers onboard the geostationary Meteosat First Generation Platform. The CDR is available from the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF). Here, it is assessed whether a homogeneous extension of the SIS CDR to the present is possible with operationally generated surface radiation data provided by CM SAF using the SEVIRI and GERB instruments onboard the Meteosat Second Generation satellites. Three extended CM SAF SIS CDR versions consisting of MVIRI-derived SIS (1983–2005) and three different SIS products derived from the SEVIRI and GERB instruments onboard the MSG satellites (2006 onwards) were tested. A procedure to detect shift inhomogeneities in the extended data record (1983–present) was applied that combines the Standard Normal Homogeneity Test (SNHT) and a penalized maximal T-test with visual inspection. Shift detection was done by comparing the SIS time series with the ground stations mean, in accordance with statistical significance. Several stations of the Baseline Surface Radiation Network (BSRN) and about 50 stations of the Global Energy Balance Archive (GEBA) over Europe were used as the ground-based reference. The analysis indicates several breaks in the data record between 1987 and 1994 probably due to artefacts in the raw data and instrument failures. After 2005 the MVIRI radiometer was replaced by the narrow-band SEVIRI and the broadband GERB radiometers and a new retrieval algorithm was applied. This induces significant challenges for the homogenisation across the satellite generations. Homogenisation is performed by applying a mean-shift correction depending on the shift size of any segment between two break points to the last segment (2006–present). Corrections are applied to the most significant breaks that can be related to satellite changes. This study focuses on the European region, but the methods can be generalized to other regions. To account for seasonal dependence of the mean-shifts the correction was performed independently for each calendar month. In comparison to the ground-based reference the homogenised data record shows an improvement over the original data record in terms of anomaly correlation and bias. In general the method can also be applied for the adjustment of satellite datasets addressing other variables to bridge the gap between CDRs and near-real-time data.

Accuracy of CM-SAF solar irradiance incident on horizontal surface

The Climate Monitoring Satellite Application Facility (CM-SAF) provides estimates of global solar irradiance incident on horizontal surface at Earth surface. Measurements performed in 2010 at five Romanian meteorological stations are used to test the accuracy of the CM-SAF irradiance data. The dataset contains null solar global irradiance values, which cannot be explained by very large values of the zenith angle neither by overcast sky conditions. Sub-databases have been created. The database Z85 consists of irradiance data, without filtering and processing. The database Z85SIS+ remove all null irradiance values. For a given database, the root mean square error (RMSE) with respect to the ground-based measurements is rather similar for all stations, i.e. around 35 % for Z85 and 24 % for Z85SIS+. On average, the database Z85SIS+ has smaller mean bias error (MBE) than the database Z85, independent of the degree of cloudiness. For the database Z85, MBE (RMSE) ranges, depending on station, between −9.4 and −1.2 % (35.3 and 39.1 %). For database Z85SIS+, the MBE (RMSE) ranges, depending on station, between −4.0 and 0.1 % (23.0 and 29.1 %). On overcast sky, we found for some stations MBE = −0.1 % and RMSE = 46.4 % when the database Z85SIS+ has been considered. The accuracy of the database Z85 is lower; we found MBE = −7.0 % and RMSE = 58.8 % as extreme cases.

Spatial representativeness of ground‐based solar radiation measurements

The validation of gridded surface solar radiation (SSR) data often relies on the comparison with ground‐based in situ measurements. This poses the question on how representative a point measurement is for a larger‐scale surrounding. We use high‐resolution (0.03°) SSR data from the Satellite Application Facility on Climate Monitoring (CM SAF) to study the subgrid spatial variability in all‐sky SSR over Europe and the spatial representativeness of 143 surface sites with homogeneous records for their site‐centered larger surroundings varying in size from 0.25° to 3°, as well as with respect to a given standard grid of 1° resolution. These analyses are done on a climatological annual and monthly mean basis over the period 2001–2005. The spatial variability of the CM SAF data set itself agrees very well with surface measurements in Europe, justifying its use for the present study. The annual mean subgrid variability in the 1° standard grid over European land is on average 1.6% (2.4 W m−2), with maximum of up to 10% in Northern Spain. The annual mean representation error of point values at 143 surface sites with respect to their 1° surrounding is on average 2% (3 W m−2). For larger surroundings of 3°, the representation error increases to 3% (4.8 W m−2). The monthly mean representation error at the surface sites with respect to the 1° standard grid is on average 3.7% (4 W m−2). This error is reduced when site‐specific correction factors are applied or when multiple sites are available in the same grid cell, i.e., three more sites reduce the error by 50%.

HelioFTH: combining cloud index principles and aggregated rating for cloud masking using infrared observations from geostationary satellites

Abstract. In this paper a cloud mask and cloud fractional coverage (CFC) retrieval scheme called HelioFTH is presented. The algorithm is self-calibrating and relies on infrared (IR) window-channel observations only. It needs no input from numerical weather prediction (NWP) or radiative transfer models, nor from other satellite platforms. The scheme is applicable to the full temporal and spatial resolution of the Meteosat Visible and InfraRed Imager (MVIRI) and the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) sensors. The main focus is laid on the separation of middle- and high-level cloud coverage (HCC) from low-level clouds based on an internal cloud-top pressure (CTP) product. CFC retrieval employs a IR-only cloud mask based on an aggregated rating scheme. CTP retrieval is based on a Heliosat-like cloud index for the MVIRI IR channel. CFC from HelioFTH, the International Satellite Cloud Climatology Project (ISCCP) DX and the Satellite Application Facility on Climate Monitoring (CM SAF) were validated with CFC from the Baseline Surface Radiation Network (BSRN) and the Alpine Surface Radiation Budget (ASRB) network. HelioFTH CFC differs by not more than 5–10% from CM SAF CFC but it is higher than ISCCP-DX CFC. In particular the conditional probability to detect cloud-free pixels with HelioFTH is raised by about 35% compared to ISCCP-DX. The HelioFTH CFC is able to reproduce the day-to-day variability observed at the surface. Also, the HelioFTH HCC was inter-compared to CM SAF and ISCCP-DX over different regions and stations. The probability of false detection of cloud-free HCC pixels is in the same order as ISCCP-DX compared to the CM SAF HCC product over the full-disk area. HelioFTH could be used for generating an independent climate data record of cloud physical properties once its consistency and homogeneity is validated for the full Meteosat time series.

Precipitation products from the hydrology SAF

Abstract. The EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF) was established by the EUMETSAT Council on 3 July 2005, starting activity on 1 September 2005. The Italian Meteorological Service serves as Leading Entity on behalf of twelve European member countries. H-SAF products include precipitation, soil moisture and snow parameters. Some products are based only on satellite observations, while other products are based on the assimilation of satellite measurements/products into numerical models. In addition to product development and generation, H-SAF includes a product validation program and a hydrological validation program that are coordinated, respectively, by the Italian Department of Civil Protection and by the Polish Institute of Meteorology and Water Management. The National Center of Aeronautical Meteorology and Climatology (CNMCA) of the Italian Air Force is responsible for operational product generation and dissemination. In this paper we describe the H-SAF precipitation algorithms and products, which have been developed by the Italian Institute of Atmospheric Sciences and Climate (in collaboration with the international community) and by CNMCA during the Development Phase (DP, 2005–2010) and the first Continuous Development and Operations Phase (CDOP-1, 2010–2012). The precipitation products are based on passive microwave measurements obtained from radiometers onboard different sun-synchronous low-Earth-orbiting satellites (especially, the SSM/I and SSMIS radiometers onboard DMSP satellites and the AMSU-A + AMSU-B/MHS radiometer suites onboard EPS-MetOp and NOAA-POES satellites), as well as on combined infrared/passive microwave measurements in which the passive microwave precipitation estimates are used in conjunction with SEVIRI images from the geostationary MSG satellite. Moreover, the H-SAF product generation and dissemination chain and independent product validation activities are described. Also, the H-SAF program and its associated activities that currently are being carried out or are planned to be performed within the second CDOP phase (CDOP-2, 2012–2017) are presented in some detail. Insofar as CDOP-2 is concerned, it is emphasized that all algorithms and processing schemes will be improved and enhanced so as to extend them to satellites that will be operational within this decade – particularly the geostationary Meteosat Third Generation satellites and the low-Earth-orbiting Core Observatory of the international Global Precipitation Measurement mission. Finally, the role of H-SAF within the international science and operations community is explained.

Night time detection of Saharan dust using infrared window channels: Application to NPP/VIIRS

A Saharan Dust Index (SDI) has been produced operationally since 2006 from Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) data. This SDI, based on brightness temperatures (BTs) at 3.9, 8.7, 10.8 and 12.0μm, has been used in the Ocean and Sea Ice Satellite Application Facility (OSI-SAF) Sea Surface Temperature (SST) chain to detect Saharan dust and correct for its effect on SST calculations. OSI-SAF has developed a processing chain to derive Sea Surface Temperature (SST) from the Suomi National Polar-orbiting partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) data. In this framework, an SDI algorithm adapted to VIIRS data has been developed. Consistency between various SDI derived from various sensors is essential in the OSI-SAF context. Without absolute definition of this parameter, we have adopted the Meteosat-9 nominal SDI values as reference. In this paper we present a new method to define SDI algorithms, based on the use of simulated BTs contaminated by aerosols at various concentrations and altitudes. This method has been applied to VIIRS data. It shows consistent results with the original 2006 method and a simple regression against Meteosat-9 SDI values when compared to the operational Meteosat-9 SDI. This new method can be used to prepare SDI equations for any sensor with adequate channels once their radiometric characteristics are known. In addition, it ensures consistency with our reference SDI.

Satellite-Based Sunshine Duration for Europe

In this study, two different methods were applied to derive daily and monthly sunshine duration based on high-resolution satellite products provided by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Climate Monitoring using data from Meteosat Second Generation (MSG) SEVIRI (Spinning Enhanced Visible and Infrared Imager). The satellite products were either hourly cloud type or hourly surface incoming direct radiation. The satellite sunshine duration estimates were not found to be significantly different using the native 15-minute temporal resolution of SEVIRI. The satellite-based sunshine duration products give additional spatial information over the European continent compared with equivalent in situ-based products. An evaluation of the satellite sunshine duration by product intercomparison and against station measurements was carried out to determine their accuracy. The satellite data were found to be within ±1 h/day compared to high-quality Baseline Surface Radiation Network or surface synoptic observations (SYNOP) station measurements. The satellite-based products differ more over the oceans than over land, mainly because of the treatment of fractional clouds in the cloud type-based sunshine duration product. This paper presents the methods used to derive the satellite sunshine duration products and the performance of the different retrievals. The main benefits and disadvantages compared to station-based products are also discussed.

CLARA-A1: a cloud, albedo, and radiation dataset from 28 yr of global AVHRR data

Abstract. A new satellite-derived climate dataset – denoted CLARA-A1 ("The CM SAF cLoud, Albedo and RAdiation dataset from AVHRR data") – is described. The dataset covers the 28 yr period from 1982 until 2009 and consists of cloud, surface albedo, and radiation budget products derived from the AVHRR (Advanced Very High Resolution Radiometer) sensor carried by polar-orbiting operational meteorological satellites. Its content, anticipated accuracies, limitations, and potential applications are described. The dataset is produced by the EUMETSAT Climate Monitoring Satellite Application Facility (CM SAF) project. The dataset has its strengths in the long duration, its foundation upon a homogenized AVHRR radiance data record, and in some unique features, e.g. the availability of 28 yr of summer surface albedo and cloudiness parameters over the polar regions. Quality characteristics are also well investigated and particularly useful results can be found over the tropics, mid to high latitudes and over nearly all oceanic areas. Being the first CM SAF dataset of its kind, an intensive evaluation of the quality of the datasets was performed and major findings with regard to merits and shortcomings of the datasets are reported. However, the CM SAF's long-term commitment to perform two additional reprocessing events within the time frame 2013–2018 will allow proper handling of limitations as well as upgrading the dataset with new features (e.g. uncertainty estimates) and extension of the temporal coverage.

English

Data from satellite application facility on&nbsp;climate&nbsp;monitoring (CM SAF) have been used to estimate and study seasonal variation of columnar refractivity in relation with relative humidity and temperature for twenty six stations categorized into four climatic regions (coastal, Guinea savannah, midland and sub Sahel regions) over Nigeria. Mean, standard error, maximum, minimum values and ranges of these variables were also determined for different regions at each atmospheric level. The results have revealed that the monthly variation of refractivity along side with meteorological factors (relative humidity and temperature) from one region to another over Nigeria are influenced by seasonal variation, geographical location and environmental situation of each region. It is also noticed that the range of relative humidity and temperature are wider in the northern part (56.19 and 72.51% and 5.64 and 5.90 K respectively) compared to the southern part (17.85 and 25.27% and 3.19 and 5.44 K respectively) of the country. This effect was observed in the variation of refractivity also. Effect of inter tropical discontinuity (ITD) was recorded in the southern variation of relative humidity and refractivity. &nbsp; Key words:&nbsp;Columnar, refractivity, meteorological, variation.

Improving NWP Model Cloud Forecasts Using Meteosat Second-Generation Imagery

Abstract The cloud mask of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) is a nowcasting Satellite Application Facility (SAF) that is used to improve initial cloudiness in the High-Resolution Limited-Area Model (HIRLAM). This cloud mask is based on images from the Meteorological Satellite (Meteosat) Second Generation (MSG) satellite. The quality of the SAF cloud mask appeared to be better than initial HIRLAM clouds in 84% of the cases. Forecasts have been performed for about a week in each of the four seasons during 2009 and 2010. Better initial clouds in HIRLAM always lead to better cloud predictions. Verification of forecasts showed that the positive impact is still present after 24 h in 59% of the cases. This is remarkable, because initial dynamics was kept unchanged. The magnitude of the positive impact on cloud predictions is more or less proportional to the initial cloud improvement, and it decreases with forecast length. Also, forecast 2-m temperatures are affected by initial clouds. The generally positive bias of the 2-m temperature errors becomes a few tenths of a degree larger during the night but it decreases a comparable amount during daylight, because MSG tends to increase the cloud amounts in HIRLAM. The standard deviation of the errors often improves slightly in the first part of the forecast, indicating that forecast temperatures correlate better with observations when MSG is used for initialization. For longer lead times, however, standard deviations deteriorate a few tenths of a degree in seven of the eight verification periods, which all had a length of about a week.

Validation and stability assessment of the monthly mean CM SAF surface solar radiation dataset over Europe against a homogenized surface dataset (1983–2005)

This work presents a validation of the downwelling surface shortwave radiation, or surface solar radiation (SSR), derived from the Satellite Application Facility on Climate Monitoring (CM SAF) over Europe for a 23-year period of records on a monthly basis. This SSR product has been recently derived based on the visible channel of the Meteosat First Generation satellites, providing a dataset with a high spatial resolution (0.03°×0.03°) covering the 1983–2005 period. The CM SAF SSR product is compared against a homogeneous dataset of surface observations from the Global Energy Balance Archive (GEBA) over Europe, which has been homogenized by means of the Standard Normal Homogeneity Test (SNHT). The results show a good agreement between both datasets (r2=0.86, p<0.01), with a slight overestimation (bias of +5.20Wm−2) of the CM SAF records as compared to the surface observations on a monthly mean basis. Equally, there is a monthly mean absolute bias difference (MABD) of 8.19Wm−2 that is below the accuracy threshold defined by the CM SAF. There is a clear maximum and minimum MABD during summer and winter, respectively, with an opposite cycle if the relative MABD values are considered. Moreover, the temporal stability of the CM SAF SSR is checked against the GEBA stations for the mean time series over Europe, as well as for each individual series. The results point to possible inhomogeneities in the CM SAF records around 1987 and 1994, possibly due to changes in the satellite instruments, although other factors such as the lack of aerosol retrievals in the CM SAF SSR are also discussed. Consequently, the study of the means and trends in the SSR derived from CM SAF is only recommended for the records after 1994.

CDRD and PNPR satellite passive microwave precipitation retrieval algorithms: EuroTRMM/EURAINSAT origins and H-SAF operations

Abstract. Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF) is a EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites) program, designed to deliver satellite products of hydrological interest (precipitation, soil moisture and snow parameters) over the European and Mediterranean region to research and operations users worldwide. Six satellite precipitation algorithms and concomitant precipitation products are the responsibility of various agencies in Italy. Two of these algorithms have been designed for maximum accuracy by restricting their inputs to measurements from conical and cross-track scanning passive microwave (PMW) radiometers mounted on various low Earth orbiting satellites. They have been developed at the Italian National Research Council/Institute of Atmospheric Sciences and Climate in Rome (CNR/ISAC-Rome), and are providing operational retrievals of surface rain rate and its phase properties. Each of these algorithms is physically based, however, the first of these, referred to as the Cloud Dynamics and Radiation Database (CDRD) algorithm, uses a Bayesian-based solution solver, while the second, referred to as the PMW Neural-net Precipitation Retrieval (PNPR) algorithm, uses a neural network-based solution solver. Herein we first provide an overview of the two initial EU research and applications programs that motivated their initial development, EuroTRMM and EURAINSAT (European Satellite Rainfall Analysis and Monitoring at the Geostationary Scale), and the current H-SAF program that provides the framework for their operational use and continued development. We stress the relevance of the CDRD and PNPR algorithms and their precipitation products in helping secure the goals of H-SAF's scientific and operations agenda, the former helpful as a secondary calibration reference to other algorithms in H-SAF's complete mix of algorithms. Descriptions of the algorithms' designs are provided including a few examples of their performance. This aspect of the development of the two algorithms is placed in the context of what we refer to as the TRMM era, which is the era denoting the active and ongoing period of the Tropical Rainfall Measuring Mission (TRMM) that helped inspire their original development. In 2015, the ISAC-Rome precipitation algorithms will undergo a transformation beginning with the upcoming Global Precipitation Measurement (GPM) mission, particularly the GPM Core Satellite technologies. A few years afterward, the first pair of imaging and sounding Meteosat Third Generation (MTG) satellites will be launched, providing additional technological advances. Various of the opportunities presented by the GPM Core and MTG satellites for improving the current CDRD and PNPR precipitation retrieval algorithms, as well as extending their product capability, are discussed.

Systematic and random errors between collocated satellite ice water path observations

AbstractThere remains large disagreement between ice‐water path (IWP) in observational data sets, largely because the sensors observe different parts of the ice particle size distribution. A detailed comparison of retrieved IWP from satellite observations in the Tropics (±30° latitude) in 2007 was made using collocated measurements. The radio detection and ranging(radar)/light detection and ranging (lidar) (DARDAR) IWP data set, based on combined radar/lidar measurements, is used as a reference because it provides arguably the best estimate of the total column IWP. For each data set, usable IWP dynamic ranges are inferred from this comparison. IWP retrievals based on solar reflectance measurements, in the moderate resolution imaging spectroradiometer (MODIS), advanced very high resolution radiometer–based Climate Monitoring Satellite Applications Facility (CMSAF), and Pathfinder Atmospheres‐Extended (PATMOS‐x) datasets, were found to be correlated with DARDAR over a large IWP range (~20–7000 g m‐2). The random errors of the collocated data sets have a close to lognormal distribution, and the combined random error of MODIS and DARDAR is less than a factor of 2, which also sets the upper limit for MODIS alone. In the same way, the upper limit for the random error of all considered data sets is determined. Data sets based on passive microwave measurements, microwave surface and precipitation products system (MSPPS), microwave integrated retrieval system (MiRS), and collocated microwave only (CMO), are largely correlated with DARDAR for IWP values larger than approximately 700 g m‐2. The combined uncertainty between these data sets and DARDAR in this range is slightly less MODIS‐DARDAR, but the systematic bias is nearly an order of magnitude.