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- Author or Editor: Otto Hyvärinen x
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Abstract
An alternative derivation of Heidke skill score for 2 × 2 tables is presented, starting from the assumption that a categorical forecast is useful, if the probability of an occurrence of an event, given the forecast, is greater than the base rate of the event. A tentative measure of skill would then be the difference of these probabilities, normalized by the maximum value based on the base rate. For binary events, the Heidke skill score is then the harmonic mean of these differences for both the occurrence and the nonoccurrence of the event. This derivation differs from the usual derivation in that the concept of chance agreement is not used. It is Bayesian in nature with implied updating of prior probabilities to posterior probabilities.
Abstract
An alternative derivation of Heidke skill score for 2 × 2 tables is presented, starting from the assumption that a categorical forecast is useful, if the probability of an occurrence of an event, given the forecast, is greater than the base rate of the event. A tentative measure of skill would then be the difference of these probabilities, normalized by the maximum value based on the base rate. For binary events, the Heidke skill score is then the harmonic mean of these differences for both the occurrence and the nonoccurrence of the event. This derivation differs from the usual derivation in that the concept of chance agreement is not used. It is Bayesian in nature with implied updating of prior probabilities to posterior probabilities.
Abstract
Snow cover plays an important role in the climate system by changing the energy and mass transfer between the atmosphere and the surface. Reliable observations of the snow cover are difficult to obtain without satellites. This paper introduces a new algorithm for satellite-based snow-cover detection that is in operational use for Meteosat in the European Organisation for the Exploitation of Meteorological Satellites Satellite Application Facility on Land Surface Analysis (LSA SAF). The new version of the product is compared with the old version and the NOAA/National Environmental Satellite, Data, and Information Service Interactive Multisensor Snow and Ice Mapping System (IMS) snow-cover product. The new version of the LSA SAF snow-cover product improves the accuracy of snow detection and is comparable to the IMS product in cloud-free conditions.
Abstract
Snow cover plays an important role in the climate system by changing the energy and mass transfer between the atmosphere and the surface. Reliable observations of the snow cover are difficult to obtain without satellites. This paper introduces a new algorithm for satellite-based snow-cover detection that is in operational use for Meteosat in the European Organisation for the Exploitation of Meteorological Satellites Satellite Application Facility on Land Surface Analysis (LSA SAF). The new version of the product is compared with the old version and the NOAA/National Environmental Satellite, Data, and Information Service Interactive Multisensor Snow and Ice Mapping System (IMS) snow-cover product. The new version of the LSA SAF snow-cover product improves the accuracy of snow detection and is comparable to the IMS product in cloud-free conditions.
Abstract
An increasing number of people leave their mark on the Internet by publishing personal notes (e.g., text, photos, videos) on Web-based services such as Facebook and Flickr. This creates a vast source of information that could be utilized in meteorology, for example, as a complement to traditional weather observations. Photo-sharing services offer an increasing amount of useful data, as modern mobile devices can automatically include coordinates and time stamps on photos, and users can easily tag them for content. In this study, different weather-related photos and their metadata were accessed from the photo-sharing service Flickr, and their reliability was assessed. Case studies of hail detection were then performed. The position of hail detected in the atmosphere by radar was compared with positions of Flickr photos depicting hail on the ground. As a result of this preliminary study, the authors think that further exploration of the use of Flickr photographs is warranted, and the consideration of other social media as data sources can be recommended.
Abstract
An increasing number of people leave their mark on the Internet by publishing personal notes (e.g., text, photos, videos) on Web-based services such as Facebook and Flickr. This creates a vast source of information that could be utilized in meteorology, for example, as a complement to traditional weather observations. Photo-sharing services offer an increasing amount of useful data, as modern mobile devices can automatically include coordinates and time stamps on photos, and users can easily tag them for content. In this study, different weather-related photos and their metadata were accessed from the photo-sharing service Flickr, and their reliability was assessed. Case studies of hail detection were then performed. The position of hail detected in the atmosphere by radar was compared with positions of Flickr photos depicting hail on the ground. As a result of this preliminary study, the authors think that further exploration of the use of Flickr photographs is warranted, and the consideration of other social media as data sources can be recommended.
Abstract
In aviation meteorology, METAR messages are used to disseminate the existence of cumulonimbus (Cb) clouds. METAR messages are traditionally constructed manually from human observations, but there is a growing trend toward automation of this process. At the Finnish Meteorological Institute (FMI), METAR messages incorporate an operational automatic detection of Cb based solely on weather radar data, when manual observations are not available. However, the verification of this automatic Cb detection is challenging, as good ground truth data are not often available; even human observations are not perfect as Cb clouds can be obscured by other clouds, for example. Therefore, statistical estimation of the relevant verification measures from imperfect observations using latent class analysis (LCA) was explored. In addition to radar-based products and human observations, the convective rainfall rate from EUMETSAT’s Nowcasting Satellite Application Facility and lightning products from the Finnish lightning network were used for determining the existence of Cb clouds. Results suggest that LCA gives reasonable estimates of verification measures and, based on these estimates, the Cb detection system at FMI gives results comparable to human observations.
Abstract
In aviation meteorology, METAR messages are used to disseminate the existence of cumulonimbus (Cb) clouds. METAR messages are traditionally constructed manually from human observations, but there is a growing trend toward automation of this process. At the Finnish Meteorological Institute (FMI), METAR messages incorporate an operational automatic detection of Cb based solely on weather radar data, when manual observations are not available. However, the verification of this automatic Cb detection is challenging, as good ground truth data are not often available; even human observations are not perfect as Cb clouds can be obscured by other clouds, for example. Therefore, statistical estimation of the relevant verification measures from imperfect observations using latent class analysis (LCA) was explored. In addition to radar-based products and human observations, the convective rainfall rate from EUMETSAT’s Nowcasting Satellite Application Facility and lightning products from the Finnish lightning network were used for determining the existence of Cb clouds. Results suggest that LCA gives reasonable estimates of verification measures and, based on these estimates, the Cb detection system at FMI gives results comparable to human observations.
Abstract
The cloud mask is an essential product derived from satellite data. Whereas cloud analysis applications typically make use of information from cloudy pixels, many other applications require cloud-free conditions. For this reason many organizations have their own cloud masks tuned to serve their particular needs. Being a fundamental product, continuous quality monitoring and validation of these cloud masks are vital. This study evaluated the performance of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Meteorological Products Extraction Facility cloud mask (MPEF), together with the Nowcasting Satellite Application Facility (SAFNWC) cloud masks provided by Météo-France (SAFNWC/MSG) and the Swedish Meteorological and Hydrological Institute (SAFNWC/PPS), in the high-latitude area of greater Helsinki in Finland. The first two used the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument from the geostationary Meteosat-8 satellite, whereas the last used the Advanced Very High Resolution Radiometer (AVHRR) instrument on board the polar-orbiting NOAA satellite series. Ceilometer data from the Helsinki Testbed, an extensive observation network covering the greater Helsinki area in Finland, were used as reference data in the cloud mask comparison. A computational method, called bootstrapping, is introduced to account for the strong temporal and spatial correlation of the ceilometer observations. The method also allows the calculation of the confidence intervals (CI) for the results. This study comprised data from February and August 2006. In general, the SAFNWC/MSG algorithm performed better than MPEF. Differences were found especially in the early morning low cloud detection. The SAFNWC/PPS cloud mask performed very well in August, better than geostationary-based masks, but had problems in February when its performance was worse. The use of the CIs gave the results more depth, and their use should be encouraged.
Abstract
The cloud mask is an essential product derived from satellite data. Whereas cloud analysis applications typically make use of information from cloudy pixels, many other applications require cloud-free conditions. For this reason many organizations have their own cloud masks tuned to serve their particular needs. Being a fundamental product, continuous quality monitoring and validation of these cloud masks are vital. This study evaluated the performance of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Meteorological Products Extraction Facility cloud mask (MPEF), together with the Nowcasting Satellite Application Facility (SAFNWC) cloud masks provided by Météo-France (SAFNWC/MSG) and the Swedish Meteorological and Hydrological Institute (SAFNWC/PPS), in the high-latitude area of greater Helsinki in Finland. The first two used the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument from the geostationary Meteosat-8 satellite, whereas the last used the Advanced Very High Resolution Radiometer (AVHRR) instrument on board the polar-orbiting NOAA satellite series. Ceilometer data from the Helsinki Testbed, an extensive observation network covering the greater Helsinki area in Finland, were used as reference data in the cloud mask comparison. A computational method, called bootstrapping, is introduced to account for the strong temporal and spatial correlation of the ceilometer observations. The method also allows the calculation of the confidence intervals (CI) for the results. This study comprised data from February and August 2006. In general, the SAFNWC/MSG algorithm performed better than MPEF. Differences were found especially in the early morning low cloud detection. The SAFNWC/PPS cloud mask performed very well in August, better than geostationary-based masks, but had problems in February when its performance was worse. The use of the CIs gave the results more depth, and their use should be encouraged.
Abstract
Modern society is very dependent on electricity. In the energy sector, the amount of renewable energy is growing, especially wind energy. To keep the electricity network in balance we need to know how much, when, and where electricity is produced. To support this goal, the need for proper wind forecasts has grown. Compared to traditional deterministic forecasts, ensemble models can better provide the range of variability and uncertainty. However, probabilistic forecasts are often either under- or overdispersive and biased, thus not covering the true and full distribution of probabilities. Hence, statistical postprocessing is needed to increase the value of forecasts. However, traditional closer-to-surface wind observations do not support the verification of wind higher above the surface that is more relevant for wind energy production. Thus, the goal of this study was to test whether new types of observations like radar and lidar winds could be used for verification and statistical calibration of 100-m winds. According to our results, the calibration improved the forecast skill compared to a raw ensemble. The results are better for low and moderate winds, but for higher wind speeds more training data would be needed, either from a larger number of stations or using a longer training period.
Abstract
Modern society is very dependent on electricity. In the energy sector, the amount of renewable energy is growing, especially wind energy. To keep the electricity network in balance we need to know how much, when, and where electricity is produced. To support this goal, the need for proper wind forecasts has grown. Compared to traditional deterministic forecasts, ensemble models can better provide the range of variability and uncertainty. However, probabilistic forecasts are often either under- or overdispersive and biased, thus not covering the true and full distribution of probabilities. Hence, statistical postprocessing is needed to increase the value of forecasts. However, traditional closer-to-surface wind observations do not support the verification of wind higher above the surface that is more relevant for wind energy production. Thus, the goal of this study was to test whether new types of observations like radar and lidar winds could be used for verification and statistical calibration of 100-m winds. According to our results, the calibration improved the forecast skill compared to a raw ensemble. The results are better for low and moderate winds, but for higher wind speeds more training data would be needed, either from a larger number of stations or using a longer training period.
Abstract
Snow cover plays a significant role in the weather and climate system by affecting the energy and mass transfer between the surface and the atmosphere. It also has far-reaching effects on ecosystems of snow-covered areas. Therefore, global snow-cover observations in a timely manner are needed. Satellite-based instruments can be utilized to produce snow-cover information that is suitable for these needs. Highly variable surface and snow-cover features suggest that operational snow extent algorithms may benefit from at least a partly empirical approach that is based on carefully analyzed training data. Here, a new two-phase snow-cover algorithm utilizing data from the Advanced Very High Resolution Radiometer (AVHRR) on board the MetOp satellites of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) is introduced and evaluated. This algorithm is used to produce the MetOp/AVHRR H32 snow extent product for the Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF). The algorithm aims at direct detection of snow-covered and snow-free pixels without preceding cloud masking. Pixels that cannot be classified reliably to snow or snow-free, because of clouds or other reasons, are set as unclassified. This reduces the coverage but increases the accuracy of the algorithm. More than four years of snow-depth and state-of-the-ground observations from weather stations were used to validate the product. Validation results show that the algorithm produces high-quality snow coverage data that may be suitable for numerical weather prediction, hydrological modeling, and other applications.
Abstract
Snow cover plays a significant role in the weather and climate system by affecting the energy and mass transfer between the surface and the atmosphere. It also has far-reaching effects on ecosystems of snow-covered areas. Therefore, global snow-cover observations in a timely manner are needed. Satellite-based instruments can be utilized to produce snow-cover information that is suitable for these needs. Highly variable surface and snow-cover features suggest that operational snow extent algorithms may benefit from at least a partly empirical approach that is based on carefully analyzed training data. Here, a new two-phase snow-cover algorithm utilizing data from the Advanced Very High Resolution Radiometer (AVHRR) on board the MetOp satellites of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) is introduced and evaluated. This algorithm is used to produce the MetOp/AVHRR H32 snow extent product for the Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF). The algorithm aims at direct detection of snow-covered and snow-free pixels without preceding cloud masking. Pixels that cannot be classified reliably to snow or snow-free, because of clouds or other reasons, are set as unclassified. This reduces the coverage but increases the accuracy of the algorithm. More than four years of snow-depth and state-of-the-ground observations from weather stations were used to validate the product. Validation results show that the algorithm produces high-quality snow coverage data that may be suitable for numerical weather prediction, hydrological modeling, and other applications.
Abstract
Snow cover has a strong effect on the surface and lower atmosphere in NWP models. Because the progress of in situ observations has stalled, satellite-based snow analyses are becoming increasingly important. Currently, there exist several products that operationally map global or continental snow cover. In this study, satellite-based snow cover analyses from NOAA, NASA, and the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), and NWP snow analyses from the High-Resolution Limited-Area Model (HIRLAM) and ECMWF, were compared using data from January to June 2006. Because no analyses were independent and since available in situ measurements were already used in the NWP analyses, no independent ground truth was available and only the consistency between analyses could be compared. Snow analyses from NOAA, NASA, and ECMWF were similar, but the analysis from NASA was greatly hampered by clouds. HIRLAM and EUMETSAT deviated most from other analyses. Even though the analysis schemes of HIRLAM and ECMWF were quite similar, the resulting snow analyses were quite dissimilar, because ECMWF used the satellite information of snow cover in the form of NOAA analyses, while HIRLAM used none. The differences are especially prominent in areas around the snow edge where few in situ observations are available. This suggests that NWP snow analyses based only on in situ measurements would greatly benefit from inclusion of satellite-based snow cover information.
Abstract
Snow cover has a strong effect on the surface and lower atmosphere in NWP models. Because the progress of in situ observations has stalled, satellite-based snow analyses are becoming increasingly important. Currently, there exist several products that operationally map global or continental snow cover. In this study, satellite-based snow cover analyses from NOAA, NASA, and the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), and NWP snow analyses from the High-Resolution Limited-Area Model (HIRLAM) and ECMWF, were compared using data from January to June 2006. Because no analyses were independent and since available in situ measurements were already used in the NWP analyses, no independent ground truth was available and only the consistency between analyses could be compared. Snow analyses from NOAA, NASA, and ECMWF were similar, but the analysis from NASA was greatly hampered by clouds. HIRLAM and EUMETSAT deviated most from other analyses. Even though the analysis schemes of HIRLAM and ECMWF were quite similar, the resulting snow analyses were quite dissimilar, because ECMWF used the satellite information of snow cover in the form of NOAA analyses, while HIRLAM used none. The differences are especially prominent in areas around the snow edge where few in situ observations are available. This suggests that NWP snow analyses based only on in situ measurements would greatly benefit from inclusion of satellite-based snow cover information.
Abstract
A physical method for estimating the instantaneous global irradiance and daily cumulative insolation based on Advanced Very High Resolution Radiometer data was developed and tested at high latitudes in a boreal subarctic region. The satellite estimates were compared with ground-based pyranometer measurements at six stations in Finland. From the comparison of instantaneous satellite estimates with 15-min average irradiances measured by pyranometers, a high correlation coefficient (0.97 in July 1996 and 0.99 in March 1997) between these estimates was obtained under clear-sky conditions. A standard error of 8% and a zero value of bias were obtained in both months. Under cloudy conditions the correlation coefficient in July 1996 was in the range of 0.79–0.83; in March 1997 it ranged from 0.89 to 0.96. The standard error in cloudy cases varied from 27% to 39% in July 1996 and from 17% to 33% in March 1997. For daily insolation estimates, the correlation coefficient had an average value of 0.95. The standard error in clear-sky cases was 7% in July 1996 and 5% in March 1997. In cloudy cases, the standard error varied from 11% to 17% in July 1996 and from 16% to 19% in March 1997. The bias was of opposite sign for the two months, ranging from +7% in July 1996 to −8% in March 1997. The satellite-based daily global radiation spatial distributions were compared in the region of Finland with those obtained by interpolating the station-based pyranometer and visual cloud observations. In southern Finland the estimates of daily global radiation based on interpolated station data and based on satellite data were of equal quality (standard error of about 10%–15%). In northern Finland, where the stations are farther apart, the satellite-based values were much more accurate (standard error of 11%–16%) than were the interpolated estimates from stations (standard error of 43%–74%).
Abstract
A physical method for estimating the instantaneous global irradiance and daily cumulative insolation based on Advanced Very High Resolution Radiometer data was developed and tested at high latitudes in a boreal subarctic region. The satellite estimates were compared with ground-based pyranometer measurements at six stations in Finland. From the comparison of instantaneous satellite estimates with 15-min average irradiances measured by pyranometers, a high correlation coefficient (0.97 in July 1996 and 0.99 in March 1997) between these estimates was obtained under clear-sky conditions. A standard error of 8% and a zero value of bias were obtained in both months. Under cloudy conditions the correlation coefficient in July 1996 was in the range of 0.79–0.83; in March 1997 it ranged from 0.89 to 0.96. The standard error in cloudy cases varied from 27% to 39% in July 1996 and from 17% to 33% in March 1997. For daily insolation estimates, the correlation coefficient had an average value of 0.95. The standard error in clear-sky cases was 7% in July 1996 and 5% in March 1997. In cloudy cases, the standard error varied from 11% to 17% in July 1996 and from 16% to 19% in March 1997. The bias was of opposite sign for the two months, ranging from +7% in July 1996 to −8% in March 1997. The satellite-based daily global radiation spatial distributions were compared in the region of Finland with those obtained by interpolating the station-based pyranometer and visual cloud observations. In southern Finland the estimates of daily global radiation based on interpolated station data and based on satellite data were of equal quality (standard error of about 10%–15%). In northern Finland, where the stations are farther apart, the satellite-based values were much more accurate (standard error of 11%–16%) than were the interpolated estimates from stations (standard error of 43%–74%).
Abstract
A statistical learning approach to produce seasonal temperature forecasts in western Europe and Scandinavia was implemented and tested. The leading principal components (PCs) of sea surface temperature (SST) and the geopotential at the 150-hPa level (GPT) were derived from reanalysis datasets and used at different lags (from one to five seasons) as predictors. Random sampling of both the fitting years and the potential predictors together with the Least Absolute Shrinkage and Selection Operator regression (LASSO) was used to create a large ensemble of statistical models. Applying the models to independent test years shows that the ensemble performs well over the target areas and that the ensemble mean is more accurate than the best individual ensemble member on average. Skillful results were especially found for summer and fall, with the anomaly correlation coefficient values ranging between 0.41 and 0.68 for these seasons. The correct simulation of decadal trends, using sufficiently long time series for fitting (70 years), and the use of lagged predictors increased the prediction skill. The decadal-scale variability of SST, most importantly the Atlantic multidecadal oscillation (AMO), and different PCs of GPT are the most important individual predictors among all predictors. Both SST and GPT bring equally much predictive power, although their importance is different in different seasons.
Abstract
A statistical learning approach to produce seasonal temperature forecasts in western Europe and Scandinavia was implemented and tested. The leading principal components (PCs) of sea surface temperature (SST) and the geopotential at the 150-hPa level (GPT) were derived from reanalysis datasets and used at different lags (from one to five seasons) as predictors. Random sampling of both the fitting years and the potential predictors together with the Least Absolute Shrinkage and Selection Operator regression (LASSO) was used to create a large ensemble of statistical models. Applying the models to independent test years shows that the ensemble performs well over the target areas and that the ensemble mean is more accurate than the best individual ensemble member on average. Skillful results were especially found for summer and fall, with the anomaly correlation coefficient values ranging between 0.41 and 0.68 for these seasons. The correct simulation of decadal trends, using sufficiently long time series for fitting (70 years), and the use of lagged predictors increased the prediction skill. The decadal-scale variability of SST, most importantly the Atlantic multidecadal oscillation (AMO), and different PCs of GPT are the most important individual predictors among all predictors. Both SST and GPT bring equally much predictive power, although their importance is different in different seasons.
