Surface Synoptic Observations Research Articles

Adjoint‐based observation impact on meteorological forecast errors in the Arctic

AbstractAccurate weather forecasting using numerical weather prediction in the Arctic remains a challenge owing to the complex atmospheric dynamics and relatively sparse observations of the region. Nevertheless, the impact of observations used for data assimilation (DA) on forecast errors (observation impacts, OIs) in the Arctic has not been fully explored. This study evaluated the OIs on 24 hr forecast errors in the Arctic by the adjoint‐based forecast sensitivity OI (FSOI) method using the Polar Weather Research and Forecasting (WRF) model, three‐dimensional variational DA, and adjoint model of WRFPLUS. The time‐averaged FSOI during August 2018 was analyzed in terms of the type, variable, satellite channel, altitude, and horizontal location of the observations. The results showed that the total FSOI was greatest for aircraft observations (AIRCRAFT), followed by sonde observations, scatterometer sea‐surface winds, advanced microwave sounding unit‐A radiance data from each satellite, and surface synoptic observations. The impact of AIRCRAFT in reducing forecast errors was greater in the Arctic than in other regions of the globe. The total FSOI of the advanced microwave sounding unit‐A radiance data from all four satellites was the greatest among the total FSOI of the individual observation types. For both AIRCRAFT and sonde observations, the FSOI per observation increased at higher latitudes, indicating that forecast errors in the Arctic could be reduced when upper atmospheric in‐situ observations at higher latitudes are used for DA. The beneficial ratio of the Global Positioning System precipitable water in the Arctic was greater than that in the midlatitudes with relatively moist atmospheres that cause greater uncertainties in converting zenith wet delay. The results of this study can contribute to configuring an optimal observing system in the Arctic by presenting the relative importance of observations for reducing forecast errors.

Operational application of Fengyun geostationary meteorological satellites to cloud observation products

Cloud products from geostationary satellites are the main alternative to surface synoptic cloud observations (SYNOP), and have become the baseline products for the development and construction of the Quality Management System (QMS) of integrated meteorological observation in China. This study addresses the needs of the China Meteorological Administration (CMA) for such an operational reform, and it is carried out using real-time observations obtained from the Fengyun-2E (FY-2E) geostationary satellite in 2012 to derive cloud total amount and classification using two different methods. Compared to surface SYNOP observations, the cloud total amount estimated by FY-2E is generally significantly lower (about 30% lower on average). The cloud classification resulting from the two methods used in this study is also significantly different from the classification obtained from surface observations (difference between 22 and 32%). The difference is smaller for the classification method, which uses additional auxiliary temperature profiles.

Implementation and tests of the NLT3D scheme in the ICON model

AbstractThe main goal of this article is to test the long‐term performance of the three‐dimensional non‐local turbulence (NLT) parameterization scheme at different grid sizes in the so‐called gray zone between classical mesoscale modeling ( several km) and large eddy simulations (LES: several 100 m). For this, NLT has been implemented in the numerical weather prediction Icosahedral Nonhydrostatic model (ICON) of Deutscher Wetterdienst (DWD). Results are compared with a one‐dimensional version of NLT (NLT) and with two operational turbulence schemes available in ICON. Comparisons with observations from radiosondes, the operational surface synoptic (SYNOP) station network, and RAdar‐OnLine‐ANeichung (RADOLAN) radar data of DWD indicate that all turbulence schemes investigated perform reasonably well. Nonetheless, a more detailed study of the model results reveals several interesting differences between the turbulence parameterizations to be discussed in detail. Median absolute errors (MAE) from point‐to‐point comparisons between numerical results and SYNOP observations tend to be smaller than those from comparisons with averaging simulated fields over an environment around each station location. This behavior indicates an information loss caused by the averaging process. For the 2‐m temperature () and the hourly precipitation sums (), MAEs decrease with decreasing grid sizes, thus suggesting an information gain for finer grids. The nighttime MAEs of and obtained with NLT and NLT are similar to or lower than those of the operational turbulence schemes of ICON. Moreover, during a shallow warm‐air intrusion, NLT and especially NLT yield a more realistic representation of the horizontal structures of and, during nighttime stable boundary‐layer situations, also . Radiosonde profiles of the potential temperature confirm a reasonable vertical mixing as obtained with NLT and NLT.

Evaluation of forecasts by a global data-driven weather model with and without probabilistic post-processing at Norwegian stations

Abstract. During the last 2 years, tremendous progress has been made in global data-driven weather models trained on numerical weather prediction (NWP) reanalysis data. The most recent models trained on the ERA5 reanalysis at 0.25° resolution demonstrate forecast quality on par with ECMWF's high-resolution model with respect to a wide selection of verification metrics. In this study, one of these models, Pangu-Weather, is compared to several NWP models with and without probabilistic post-processing for 2 m temperature and 10 m wind speed forecasting at 183 Norwegian SYNOP (surface synoptic observation) stations up to +60 h ahead. The NWP models included are the ECMWF HRES, ECMWF ENS and the HARMONIE-AROME ensemble model MEPS with 2.5 km spatial resolution. Results show that the performances of the global models are on the same level, with Pangu-Weather being slightly better than the ECMWF models for temperature and slightly worse for wind speed. The MEPS model clearly provided the best forecasts for both parameters. The post-processing improved the forecast quality considerably for all models but to a larger extent for the coarse-resolution global models due to stronger systematic deficiencies in these. Apart from this, the main characteristics in the scores were more or less the same with and without post-processing. Our results thus confirm the conclusions from other studies that global data-driven models are promising for operational weather forecasting.

Estimating hub-height wind speed based on a machine learning algorithm: implications for wind energy assessment

Abstract. Accurate estimation of wind speed at wind turbine hub height is of significance for wind energy assessment and exploitation. Nevertheless, the traditional power law method (PLM) generally estimates the hub-height wind speed by assuming a constant exponent between surface and hub-height wind speed. This inevitably leads to significant uncertainties in estimating the wind speed profile especially under unstable conditions. To minimize the uncertainties, we here use a machine learning algorithm known as random forest (RF) to estimate the wind speed at hub heights such as at 120 m (WS120), 160 m (WS160), and 200 m (WS200). These heights go beyond the traditional wind mast limit of 100–120 m. The radar wind profiler and surface synoptic observations at the Qingdao station from May 2018 to August 2020 are used as key inputs to develop the RF model. A deep analysis of the RF model construction has been performed to ensure its applicability. Afterwards, the RF model and the PLM model are used to retrieve WS120, WS160, and WS200. The comparison analyses from both RF and PLM models are performed against radiosonde wind measurements. At 120 m, the RF model shows a relatively higher correlation coefficient R of 0.93 and a smaller RMSE of 1.09 m s−1, compared with the R of 0.89 and RMSE of 1.50 m s−1 for the PLM. Notably, the metrics used to determine the performance of the model decline sharply with height for the PLM model, as opposed to the stable variation for the RF model. This suggests the RF model exhibits advantages over the traditional PLM model. This is because the RF model considers well the factors such as surface friction and heat transfer. The diurnal and seasonal variations in WS120, WS160, and WS200 from RF are then analyzed. The hourly WS120 is large during daytime from 09:00 to 16:00 local solar time (LST) and reach a peak at 14:00 LST. The seasonal WS120 is large in spring and winter and is low in summer and autumn. The diurnal and seasonal variations in WS160 and WS200 are similar to those of WS120. Finally, we investigated the absolute percentage error (APE) of wind power density between the RF and PLM models at different heights. In the vertical direction, the APE is gradually increased as the height increases. Overall, the PLM algorithm has some limitations in estimating wind speed at hub height. The RF model, which combines more observations or auxiliary data, is more suitable for the hub-height wind speed estimation. These findings obtained here have great implications for development and utilization in the wind energy industry in the future.

MODIS Soil Tillage Indexから推定した枯れ草植被率のダスト発生への影響：地上実況気象通報式(SYNOP)データによる検証

MODIS Soil Tillage Indexから推定した枯れ草植被率のダスト発生への影響：地上実況気象通報式(SYNOP)データによる検証

A transformer neural network for predicting near‐surface temperature

AbstractA new method based on the Transformer model is proposed for post‐processing of numerical weather prediction (NWP) forecasts of 2 m air temperature. The Transformer is a machine learning (ML) model based on self‐attention, which extracts information about which inputs are most important for the prediction. It is trained using time series input from NWP variables and crowd‐sourced 2 m air temperature observations from more than 1000 private weather stations (PWSs). The performance of the new post‐processing model is evaluated using both observational data from PWSs and completely independent observations from the Danish Meteorological Institute (DMI) network of surface synoptic observations (SYNOP) stations. The performance of the Transformer model is compared against the raw NWP forecast, as well as against two benchmark post‐processing models; a linear regression (LR) model and a neural network (NN). The results evaluated using PWS observations show an improvement in the 2 m temperature forecasts with respect to both bias and standard deviation (STD) for all three post‐processing models, with the Transformer model showing the largest improvement. The raw NWP forecast, LR, NN and Transformer model have a bias and STD of 0.34 and 1.96°C, 0.03 and 1.63°C, 0.10 and 1.53°C and 0.02 and 1.13°C, respectively. The corresponding results using DMI SYNOP stations also show improved forecasts, where the Transformer model performs better than both the raw NWP forecast and the two benchmark models. However, a dependence on distance to the coast and cold temperatures is observed.

A climatological study of fog in Turkey

AbstractIn this study, climatological properties of fog types, their association with surrounding environment and background synoptic mechanisms triggering fog formation are investigated for Turkey. For this purpose, SYNOP (surface synoptic observations) and METAR (meteorological aerodrome report) codes of 105 stations are used for the period 2014–2019. While fog types are separated to the fog, quasi‐fog, and dense fog events according to horizontal visibility conditions, stations are classified as mountain, seaside, urban, and rural by considering the surrounding environment. Synoptic mechanisms causing the occurrence of springtime marine fog events over Black Sea are investigated using NCEP/NCAR Reanalysis. According to the main results, highest numbers of fog events are observed during winter, spring, fall, and summer months, respectively. Radiation fog frequently occurs in the mountainous regions (i.e., central and eastern part) of Turkey during winter. As a result of the rainfall during daytime, significant amount of moisture begins to saturation at night owing to rapid cooling processes and fog layer forms just above the surface. During spring, Black Sea‐effect marine fog events are frequently shown in the seaside stations of the Black Sea and northeastern Marmara regions. On the western Black Sea, winds from northeast enable rising of cold sea water to the surface (upwelling) and transfer it to the relatively warm land areas. Denser humid and cold air settling on the surface forces warm land air to rise. Additionally, warm air advection at 850 hPa (low level) creates a thick inversion layer over the region. For the eastern Black Sea, relatively cold and humid sea surface transferred to the coasts by light local northwesterly winds encounters with the mountain barrier and is trapped in the region. Furthermore, southerly winds (850 hPa) cross over the Kaçkar Mountains cause föehn effect and generate an inversion layer over the fog layer.

Determination of Surface Precipitation Type Based on the Data Fusion Approach

Hazardous events related to atmospheric precipitation depend not only on the intensity of surface precipitation, but also on its type. Uncertainty related to determination of the precipitation type (PT) leads to financial losses in many areas of human activity, such as the power industry, agriculture, transportation, and many more. In this study, we use machine learning (ML) algorithms with the data fusion approach to more accurately determine surface PT. Based on surface synoptic observations, ERA5 reanalysis, and radar data, we distinguish between liquid, mixed, and solid precipitation types. The study domain considers the entire area of Poland and a period from 2015 to 2017. The purpose of this work is to address the question: “How can ML techniques applied in observational and NWP data help to improve the recognition of the surface PT?” Despite testing 33 parameters, it was found that a combination of the near-surface air temperature and the depth of the warm layer in the 0–1000 m above ground level (AGL) layer contains most of the signal needed to determine surface PT. The accrued probability of detection for liquid, solid, and mixed PTs according to the developed Random Forest model is 98.0%, 98.8%, and 67.3%, respectively. The application of the ML technique and data fusion approach allows to significantly improve the robustness of PT prediction compared to commonly used baseline models and provides promising results for operational forecasters.

Estimation of Wind Speed and Roughness Length Using Smartphones: Method and Quality Assessment

AbstractCrowdsourced data are now seen as a potential source of high-resolution observations in the atmospheric sciences. In this paper we investigate a potential data source, wind observations obtained using anemometers connected to handheld smartphones. The aim of this paper is twofold: to assess the quality of raw and height-extrapolated wind measurements from the handheld anemometer against professional-grade surface synoptic observation (SYNOP) stations, and to use these data of opportunity to infer a more accurate estimation of terrain roughness lengths. Roughness lengths are essential in numerical weather prediction; however, they are often poorly determined. Roughness lengths are also necessary when correcting near-surface wind observations for height offsets. For the analysis we performed a series of field experiments measuring wind profiles using handheld anemometers at roughly 2 m above ground. These raw measurements were then extrapolated to 10-m height using roughness lengths from three different sources. The extrapolation enabled us to compare the quality of roughness lengths estimated from smartphone measurements with those from traditional sources, as well as to assess the quality of these wind measurements against the professional-grade stations. We find that the handheld wind measurements are comparable in quality to wind measurements from SYNOP stations at 10-m height and that for some cases the handheld measurements can be more representative than SYNOP stations only about a kilometer away. To determine the roughness lengths, we examine a method that is based on the turbulent intensity derived from the high-frequency signal of the smartphone wind measurements. Under certain circumstances, the roughness lengths obtained with the approach presented here are superior to traditional sources.

Performance of precipitation products obtained from combinations of satellite and surface observations

ABSTRACT Knowing the spatiotemporal distribution of precipitation is undoubtedly important for planning various economic/social activities, such as agriculture, livestock, and energy production. The coarse observation density over certain regions may significantly compromise the quality of precipitation products interpolated by only surface observations. To minimize the lack of observations over certain regions, the Centre for Weather Forecast and Climate Studies (CPTEC) of National Institute for Space Research (INPE) developed two types of blended precipitation products, namely, the Combined Scheme (CoSch) and MERGE, which combine observed precipitation data with satellite estimates on a daily scale. To understand how different blending methodologies impact the final results, a comparison of each algorithm with independent rain gauges was performed with a focus over the Brazilian territory. Both products were generated at a 10-km horizontal resolution using input data from the Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG-Early) for product (Version 5) in conjunction with surface observations from Surface Synoptic Observations (SYNOP), data collection platforms (DCPs) and data from regional meteorological centres. The cumulative 24-hour precipitation was evaluated for the period from June 2014 to June 2017. The results show that both products reliably characterize the precipitation regimes over most of the study regions, although MERGE and CoSch tend to over- and underestimate the amount of precipitation, respectively. However, the magnitude of the Bias achieved by MERGE is smaller than that achieved by CoSch. Overall, MERGE outperforms CoSch when analysing rain/no rain and light to moderate rainfall (0.5 to 20.0 mm). For heavy precipitation (>35.0 mm), the performance of both products is similar. The most significant differences between the two products occur over the Northeast Region of Brazil (R3 and R4), where CoSch tends to encounter difficulties characterizing the precipitation regime during the northeastern wet period (April – November). In R3 and R4, MERGE relies more on surface observations, whereas CoSch relies on GPM-IMERG-Early, which could be associated with the deficiency of GPM-IMERG-Early in estimating the amount of precipitation associated with warm clouds.

A Preliminary Impact Study of Wind on Assimilation and Forecast Systems into the One-Dimensional Fog Forecasting Model COBEL-ISBA over Morocco

The assimilation impact of wind data from aircraft measurements (AMDAR), surface synoptic observations (SYNOP) and 3D numerical weather prediction (NWP) mesoscale model, on short-range numerical weather forecasting (up to 12 h) and on the assimilation system, using the one-dimensional fog forecasting model COBEL-ISBA (Code de Brouillard à l’Échelle Locale-Interactions Soil Biosphere Atmosphere), is studied in the present work. The wind data are extracted at Nouasseur airport, Casablanca, Morocco, over a winter period from the national meteorological database. It is the first time that wind profiles (up to 1300 m) are assimilated in the framework of a single-column model. The impact is assessed by performing NWP experiments with data denial tests, configured to be close to the operational settings. The assimilation system estimates the flow-dependent background covariances for each run of the model and takes the cross-correlations between temperature, humidity and wind components into account. When assimilated into COBEL-ISBA with an hourly update cycle, the wind field has a positive impact on temperature and specific humidity analysis and forecasts accuracy. Thus, a superior fit of the analysis background fields to observations is found when assimilating AMDAR without NWP wind data. The latter has shown a detrimental impact in all experiments. Besides, wind assimilation gave a clear improvement to short-range forecasts of near-surface thermodynamical parameters. Although, assimilation of SYNOP and AMDAR wind measurements slightly improves the probability of detection of fog but also increases the false alarms ratio by a lower magnitude.

Effects of Model Horizontal Grid Resolution on Short- and Medium-Term Daily Temperature Forecasts for Energy Consumption Application in European Cities

A short-term forecast of energy consumption is affected by different factors related to the demand in residential, commercial, thermoelectric, and industrial sectors. This demand can be strongly constrained by weather variables, especially temperatures, whose forecast may be very useful to predict the balances between supply and demand, minimizing the risk of price volatility. Energy companies use the relationship between meteorological forecast output and energy request to provide an effective scheduling of national gas and power grids and reduce operational costs in critical periods. This work reports a comparison analysis for short- and medium-term daily temperature forecasts during the period 2013-2014 by using the weather model e-kmf™ (eni-kassandra meteo forecast), currently adopted in gas and power applications where meteorological output has a key role. This weather forecast system uses different models and initial data to develop probabilistic predictions from a perspective of eleven days ahead. In particular, a set of model runs with horizontal grid spacing of 5.5, 8, 13, and 18 km with the same domain size are undertaken to assess the sensitivity of temperature to horizontal resolutions. A nonlinear Kalman filter has been also applied to postprocess forecasted data in eight European cities (Milano, Roma, Torino, Napoli, Munich, Paris, Brussels, and London). Filtered forecasts over these cities have been compared to local observations taken from SYNOP (surface synoptic observations) and METAR (meteorological Aerodrome Report) stations. Skill scores of performance have been used to generally assess the forecast reliability up to day +11. In order to understand the sensitivity to the horizontal resolution, investigations have been carried out even during four specific periods of two weeks with stable and unstable weather conditions.

Long-term changes in precipitation phase in Europe in cold half year

The change in precipitation phase is an important manifestation of climate change, especially in the moderate climate zone and mountain regions. We analyse changes in precipitation at 107 European stations in cold months (November–April). Stations in Czechia where both surface synoptic observations (SYNOP) reporting the precipitation phase, and temperature and precipitation data are available in sufficient quality, are used to determine the regional threshold temperature. The threshold temperature is then applied to separate solid and liquid precipitation phase in cold months at European stations in 1961–2010. If daily mean temperature is higher than the regional threshold temperature, daily precipitation amount is considered to be liquid and vice versa. The value of the regional threshold temperature is 1.1 °C. The ratio of solid to total precipitation (S/P) significantly declines over large parts of Europe. The strongest negative trend is detected in the belt stretching from the coast of northern Scandinavia through central and eastern Europe towards southern and southeastern Europe. The trend of S/P ranges from −0.2 to −0.9%·year−1 there. The average annual trend of the ratio of solid precipitation for all Europe is −0.12%·year−1. The magnitude of the trend of the ratio of solid precipitation exhibits only a minor sensitivity to the choice of the threshold temperature. A comparison with real trends, calculated for stations in Czechia, indicates that trends determined from the threshold temperature tend to be overestimated, that is, the decline of solid precipitation in Europe is even faster that what results of this study indicate.

Temporal Characterization of Dust Activity in the Central Patagonia Desert (Years 1964–2017)

AbstractDust emitted from the southern end of South America (SSA) is transported long distances over the Southern Ocean and deposited over this marine ecosystem. Whether the nutrients released through dissolution have a biogeochemical impact is a question with biological as well as climate implications, yet there is no clear answer. Additionally, the provenance of dust recently found in accumulated snow in East Antarctica is still a matter of debate. The Patagonia desert in SSA is the likely source, but there are no detailed records documenting dust activity in this area, thereby preventing any definite assessments. Here we provide a survey of modern dust activity of the largest dust source in SSA, the lake Colhué Huapi in central Patagonia. We analyzed five decades (1964–2017) of surface synoptic observations (World Meteorological Weather Present weather codes) and concurrent satellite aerosol detection (UV Aerosol Index from the Total Ozone Monitoring Sensor and Ozone Monitoring Instrument detectors, 1978–2017). We assessed the seasonal, year‐to‐year variability and periods of major dust activity. Several periods of enhanced activity were found with roughly 2‐ to 10‐year duration each (1970–1976, 1989–1994, 1996–1997, and 1999–2017). While dust activity peaks during summer months, wintertime activity during the most active years can well exceed the summer average of nonactive years. For a period of coincident satellite observations, the occurrence of at least three periods of high activity is confirmed. Since satellite detection is more sensitive to mesoscale dust events, the large events that occurred during these periods brought abundant dust into the SW South Atlantic. Satellites with polar orbits tend to under detect dust events in this region. Significant cloudiness obstructs the direct view of dust, and dust activity tends to occur late in the afternoon after the overpass of polar satellites. These observations have a time span adequate for comparison with transport models and modern records of dust samples collected in East Antarctica. The results contribute to a better understanding of the dynamic of modern dust transport in the Southern Hemisphere, the provenance of dust found in Antarctica, and the provenance of eolian nutrients into the Southern Ocean.

A cloud identification algorithm over the Arctic for use with AATSR–SLSTR measurements

Abstract. The accurate identification of the presence of cloud in the ground scenes observed by remote-sensing satellites is an end in itself. The lack of knowledge of cloud at high latitudes increases the error and uncertainty in the evaluation and assessment of the changing impact of aerosol and cloud in a warming climate. A prerequisite for the accurate retrieval of aerosol optical thickness (AOT) is the knowledge of the presence of cloud in a ground scene. In our study, observations of the upwelling radiance in the visible (VIS), near infrared (NIR), shortwave infrared (SWIR) and the thermal infrared (TIR), coupled with solar extraterrestrial irradiance, are used to determine the reflectance. We have developed a new cloud identification algorithm for application to the reflectance observations of the Advanced Along-Track Scanning Radiometer (AATSR) on European Space Agency (ESA)-Envisat and Sea and Land Surface Temperature Radiometer (SLSTR) on board the ESA Copernicus Sentinel-3A and -3B. The resultant AATSR–SLSTR cloud identification algorithm (ASCIA) addresses the requirements for the study AOT at high latitudes and utilizes time-series measurements. It is assumed that cloud-free surfaces have unchanged or little changed patterns for a given sampling period, whereas cloudy or partly cloudy scenes show much higher variability in space and time. In this method, the Pearson correlation coefficient (PCC) parameter is used to measure the “stability” of the atmosphere–surface system observed by satellites. The cloud-free surface is classified by analysing the PCC values on the block scale 25×25 km2. Subsequently, the reflection at 3.7 µm is used for accurate cloud identification at scene level: with areas of either 1×1 or 0.5×0.5 km2. The ASCIA data product has been validated by comparison with independent observations, e.g. surface synoptic observations (SYNOP), the data from AErosol RObotic NETwork (AERONET) and the following satellite products: (i) the ESA standard cloud product from AATSR L2 nadir cloud flag; (ii) the product from a method based on a clear-snow spectral shape developed at IUP Bremen (Istomina et al., 2010), which we call ISTO; and (iii) the Moderate Resolution Imaging Spectroradiometer (MODIS) products. In comparison to ground-based SYNOP measurements, we achieved a promising agreement better than 95 % and 83 % within ±2 and ±1 okta respectively. In general, ASCIA shows an improved performance in comparison to other algorithms applied to AATSR measurements for the identification of clouds in a ground scene observed at high latitudes.

4DVAR assimilation of GNSS zenith path delays and precipitable water into a numerical weather prediction model WRF

Abstract. The GNSS data assimilation is currently widely discussed in the literature with respect to the various applications for meteorology and numerical weather models. Data assimilation combines atmospheric measurements with knowledge of atmospheric behavior as codified in computer models. With this approach, the “best” estimate of current conditions consistent with both information sources is produced. Some approaches also allow assimilating the non-prognostic variables, including remote sensing data from radar or GNSS (global navigation satellite system). These techniques are named variational data assimilation schemes and are based on a minimization of the cost function, which contains the differences between the model state (background) and the observations. The variational assimilation is the first choice for data assimilation in the weather forecast centers, however, current research is consequently looking into use of an iterative, filtering approach such as an extended Kalman filter (EKF). This paper shows the results of assimilation of the GNSS data into numerical weather prediction (NWP) model WRF (Weather Research and Forecasting). The WRF model offers two different variational approaches: 3DVAR and 4DVAR, both available through the WRF data assimilation (WRFDA) package. The WRFDA assimilation procedure was modified to correct for bias and observation errors. We assimilated the zenith total delay (ZTD), precipitable water (PW), radiosonde (RS) and surface synoptic observations (SYNOP) using a 4DVAR assimilation scheme. Three experiments have been performed: (1) assimilation of PW and ZTD for May and June 2013, (2) assimilation of PW alone; PW, with RS and SYNOP; ZTD alone; and finally ZTD, with RS and SYNOP for 5–23 May 2013, and (3) assimilation of PW or ZTD during severe weather events in June 2013. Once the initial conditions were established, the forecast was run for 24 h. The major conclusion of this study is that for all analyzed cases, there are two parameters significantly changed once GNSS data are assimilated in the WRF model using GPSPW operator and these are moisture fields and rain. The GNSS observations improves forecast in the first 24 h, with the strongest impact starting from a 9 h lead time. The relative humidity forecast in a vertical profile after assimilation of ZTD shows an over 20 % decrease of mean error starting from 2.5 km upward. Assimilation of PW alone does not bring such a spectacular improvement. However, combination of PW, SYNOP and radiosonde improves distribution of humidity in the vertical profile by maximum of 12 %. In the three analyzed severe weather cases PW always improved the rain forecast and ZTD always reduced the humidity field bias. Binary rain analysis shows that GNSS parameters have significant impact on the rain forecast in the class above 1 mm h−1.

Implementation and evaluation of a convective/stratiform precipitation scheme in Attica region, Greece

Attica, which is the region with the largest population in Greece, suffers from numerous high rainfall intensity events inducing flash floods and, consequently, significant damages in the urbanized areas. High rainfall intensities are frequently connected with convection, a quiet often phenomenon during either the transitional seasons or the late summer thunderstorms. The objective of this paper is to implement and evaluate a stratiform-convective classification technique for the rainfall events occurred in Attica during the period 2005–2016. As this methodology requires only the precipitation records, the satisfactory evaluation of this classification approach allows further applications, such as, a climatological analysis regarding convective and stratiform trends in the study area and generally in Greece. Particularly, the rain gauge stations' records used correspond to 10-minute time series from 32 rainfall stations for the same period, and these data are analyzed and processed in order to fit a suitable exponential curve describing the stratiform component. A critical annual threshold of rainfall intensity in mm/6h is then calculated, separating the prevailing convective and stratiform regime above and below this value, respectively. The analysis is performed in three individual subareas of Attica region demarcated initially according to their morphological characteristics and subsequently by checking the cross-correlation for each pair of stations. This critical intensity threshold is determined for each year separately, in order to illustrate the uncertainty and the difficulty in identifying a standard threshold, as it is related to the annual behavior of precipitation. Using this threshold, a total of about 180 six-hour case events is detected as purely convective cases, however this number ranges for each subarea. For these cases, a further analysis is performed with the aim of verifying their convective character. Verification includes time-accurate lightning data, surface synoptic observations from the Hellenic National Meteorological Service and six-hour surface analysis charts from UK MetOffice. Findings, which are presented via indicative verification indices, show in general encouraging performance, considering the highly variable nature of convective precipitation which poses considerable problems in its identification with quite simple techniques.

Correction of CCI cloud data over the Swiss Alps using ground-based radiation measurements

Abstract. The validation of long-term cloud data sets retrieved from satellites is challenging due to their worldwide coverage going back as far as the 1980s. A trustworthy reference cannot be found easily at every location and every time. Mountainous regions present a particular problem since ground-based measurements are sparse. Moreover, as retrievals from passive satellite radiometers are difficult in winter due to the presence of snow on the ground, it is particularly important to develop new ways to evaluate and to correct satellite data sets over elevated areas. In winter for ground levels above 1000 m (a.s.l.) in Switzerland, the cloud occurrence of the newly released cloud property data sets of the ESA Climate Change Initiative Cloud_cci Project (Advanced Very High Resolution Radiometer afternoon series (AVHRR-PM) and Moderate-Resolution Imaging Spectroradiometer (MODIS) Aqua series) is 132 to 217 % that of surface synoptic (SYNOP) observations, corresponding to a rate of false cloud detections between 24 and 54 %. Furthermore, the overestimations increase with the altitude of the sites and are associated with particular retrieved cloud properties. In this study, a novel post-processing approach is proposed to reduce the amount of false cloud detections in the satellite data sets. A combination of ground-based downwelling longwave and shortwave radiation and temperature measurements is used to provide independent validation of the cloud cover over 41 locations in Switzerland. An agreement of 85 % is obtained when the cloud cover is compared to surface synoptic observations (90 % within ± 1 okta difference). The validation data are then co-located with the satellite observations, and a decision tree model is trained to automatically detect the overestimations in the satellite cloud masks. Cross-validated results show that 62±13 % of these overestimations can be identified by the model, reducing the systematic error in the satellite data sets from 14.4±15.5 % to 4.3±2.8 %. The amount of errors is lower, and, importantly, their distribution is more homogeneous as well. These corrections happen at the cost of a global increase of 7±2 % of missed clouds. Using this model, it is possible to significantly improve the cloud detection reliability in elevated areas in the Cloud_cci AVHRR-PM and MODIS-Aqua products.

Effect of Hydro-meteorological and Surface Conditions on Variations in the Frequency of Asian Dust Events

The effects of hydro-meteorological and surface variables on the frequency of Asian dust events (FAE) were investigated using ground station and satellite-based data. Present weather codes 7, 8, and 9 derived from surface synoptic observations (SYNOP) were used for counting FAE. Surface wind speed (SWS), air temperature (Ta), relative humidity (RH), and precipitation were analyzed as hydro-meteorological variables for FAE. The Normalized Difference Vegetation Index (NDVI), land surface temperature (LST), and snow cover fraction (SCF) were used to consider the effects of surface variables on FAE. The relationships between FAE and hydro-meteorological variables were analyzed using Z-score and empirical orthogonal function (EOF) analysis. Although all variables expressed the change of FAE, the degrees of expression were different. SWS, LST, and Ta (indices applicable when Z-score was 0. EOF analysis for the FAE showed the seasonal cycle, change pattern, and surface influences related to dryness condition for the FAE. The intensity of SWS was the main cause for change of FAE, but surface variables such as LST, SCF, and NDVI also were expressed because wet surface conditions suppress FAE. These results demonstrate that not only SWS and precipitation, but also surface variables, are important and useful precursors for monitoring Asian dust events.