Hemispheric transport of air pollutants can have a significant impact on regional air quality, as well as on the effect of air pollutants on regional climate. An accurate representation of hemispheric transport in regional chemical transport models (CTMs) depends on the specification of the lateral boundary conditions (LBCs). This study focuses on the methodology for evaluating LBCs of two moderately long-lived trace gases, carbon monoxide (CO) and ozone (O-3), for the European model domain and over a 7-year period, 2006-2012. The method is based on combining the use of satellite observations at the lateral boundary with the use of both satellite and in situ ground observations within the model domain. The LBCs are generated by the global European Monitoring and Evaluation Programme Meteorological Synthesizing Centre - West (EMEP MSC-W) model; they are evaluated at the lateral boundaries by comparison with satellite observations of the Terra-MOPITT (Measurements Of Pollution In The Troposphere) sensor (CO) and the Aura-OMI (Ozone Monitoring Instrument) sensor (O-3). The LBCs from the global model lie well within the satellite uncertainties for both CO and O-3. The biases increase below 700 hPa for both species. However, the satellite retrievals below this height are strongly influenced by the a priori data; hence, they are less reliable than at, e.g. 500 hPa. CO is, on average, underestimated by the global model, while O-3 tends to be overestimated during winter, and underestimated during summer. A regional CTM is run with (a) the validated monthly climatological LBCs from the global model; (b) dynamical LBCs from the global model; and (c) constant LBCs based on in situ ground observations near the domain boundary. The results are validated against independent satellite retrievals from the Aqua-AIRS (Atmospheric InfraRed Sounder) sensor at 500 hPa, and against in situ ground observations from the Global Atmospheric Watch (GAW) network. It is found that (i) the use of LBCs from the global model gives reliable in-domain results for O-3 and CO at 500 hPa. Taking AIRS retrievals as a reference, the use of these LBCs substantially improves spatial pattern correlations in the free troposphere as compared to results obtained with fixed LBCs based on ground observations. Also, the magnitude of the bias is reduced by the new LBCs for both trace gases. This demonstrates that the validation methodology based on using satellite observations at the domain boundary is sufficiently robust in the free troposphere. (ii) The impact of the LBCs on ground concentrations is significant only at locations in close proximity to the domain boundary. As the satellite data near the ground mainly reflect the a priori estimate used in the retrieval procedure, they are of little use for evaluating the effect of LBCs on ground concentrations. Rather, the evaluation of ground-level concentrations needs to rely on in situ ground observations. (iii) The improvements of dynamic over climatological LBCs become most apparent when using accumulated ozone over threshold 40 ppb (AOT40) as a metric. Also, when focusing on ground observations taken near the inflow boundary of the model domain, one finds that the use of dynamical LBCs yields a more accurate representation of the seasonal variation, as well as of the variability of the trace gas concentrations on shorter timescales.

A gridded dataset (SMHI Gridded Climatology - SMHIGridClim) has been produced forthe years 1961 - 2018 over an area covering the Nordic countries on a grid with 2.5 kmhorizontal resolution. The variables considered are the two meter temperature and twometer relative humidity on 1, 3 or 6 hour resolution, varying over the time periodcovered, the daily minimum and maximum temperatures, the daily precipitation and thedaily snow depth. The gridding was done using optimal interpolation with the gridppopen source software from the Norwegian Meteorological Institute.Observations for the analysis are provided by the Swedish, Finish and Norwegianmeteorological institutes, and the ECMWF. The ECA&D observation data set (e.g. usedfor the gridded E-OBS dataset) was considered for inclusion but was left out because ofcomplications with time stamps and accumulation periods varying between countries andperiods. Quality check of the observations was performed using the open source softwareTITAN, also developed at the Norwegian Meteorological Institute.The first guess to the optimal interpolation was given by statistically downscaledforecasts from the UERRA-HARMONIE reanalysis at 11 km horizontal resolution. Thedownscaling was done to fit the output from the operational MEPS NWP system at 2.5km with a daily and yearly variation in the downscaling parameters.The quality of the SMHIGridClim dataset, in terms of annual mean RMSE, was shown tobe similar to that of gridded datasets covering the other Nordic countries; “seNorge”from Norway and the dataset “FMI_ClimGrid” from Finland.

A new model for making probability forecasts of accumulated spot precipitation from weather radar data is presented. The model selects a source region upwind of the forecast spot. All pixels (horizontal size 2 x 2 km2) within the source region are considered, having the same probability of hitting the forecast spot. A pixel hitting the forecast spot is supposed to precipitate there a short time (about 10 min.). A drawing is performed, and a frequency distribution of accumulated precipitation during the first time step of the forecast is obtained. A second drawing gives the frequency distribution of accumulated precipitation during the first to second time step, a third one during the first to third, and so on until the end of the forecast period is reached. A number of forecasts for 1-h accumulated precipitation, with lead times of 0, 1, and 2 h, have been performed and verified. The forecasts for 0-h lead time got the highest Brier skill scores, +50% to 60% relative to climatological forecasts for accumulated precipitation below 1 mm.

Results from an intercomparison campaign of ultraviolet spectroradiometers that was organized at Nea Michaniona, Greece July, 1-13 1997, are presented. Nineteen instrument systems from 15 different countries took part and provided spectra of global solar UV irradiance for two consecutive days from sunrise to sunset every half hour. No data exchange was allowed between participants in order to achieve absolutely independent results among the instruments. The data analysis procedure included the determination of wavelength shifts and the application of suitable corrections to the measured spectra, their standardization to common spectral resolution of 1 nm full width at half maximum and the application of cosine corrections. Reference spectra were calculated for each observational time, derived for a set of instruments which were objectively selected and used as comparison norms for the assessment of the relative agreement among the various instruments. With regard to the absolute irradiance measurements, the range of the deviations from the reference for all spectra was within +/- 20%. About half of the instruments agreed to within +/-5%, while only three fell outside the +/- 10% agreement limit. As for the accuracy of the wavelength registration of the recorded spectra, for most of the spectroradiometers (14) the calculated wavelength shifts were smaller than 0.2 nm. The overall outcome of the campaign was very encouraging, as it was proven that the agreement among the majority of the instruments was good and comparable to the commonly accepted uncertainties of spectral UV measurements. In addition, many of the instruments provided consistent results relative to at least the previous two intercomparison campaigns, held in 1995 in Ispra, Italy and in 1993 in Garmisch-Partenkirchen, Germany. As a result of this series of intercomparison campaigns, several of the currently operating spectroradiometers operating may be regarded as a core group Of instruments, which with the employment of proper operational procedures are capable of providing quality spectral solar UV measurements.

This paper describes a study to evaluate the variability of radio-propagation conditions and to assess their effects upon weather-radar beam blockage corrections for precipitation estimates. Radiosonde observations are examined in order to analyse the propagation conditions at several locations covered by the Nordic Weather Radar Network (NORDRAD). A beam-propagation model is used to simulate the interaction between the radar beam and the topography and to derive correction factors. The model is applied to correct yearly accumulations, assuming standard radio-propagation conditions, and is also used to examine case studies in detail under various propagation conditions. The correction reduces the bias between yearly radar precipitation estimates and gauge records by 1 dB for moderate blockages (1% to 50%), and by up to 3 dB for severe blockages (50% to 70%). The case studies indicate that HIRLAM forecasts show potential to predict the radar coverage and the associated ground- and sea-clutter patterns. This research aims at determining a beam-blockage-correction algorithm to be used within the NORDRAD quality-control system. This is particularly useful for obtaining radar precipitation estimates in environments with complex topography. Copyright (C) 2007 Royal Meteorological Society.

We describe a method to remotely sense precipitation and classify its intensity over water, coasts and land surfaces. This method is intended to be used in an operational nowcasting environment. It is based on data obtained from the Advanced Microwave Sounding Unit (AMSU) onboard NOAA-15. Each observation is assigned a probability of belonging to four classes: precipitation-free, risk of precipitation, precipitation between 0.5 and 5 mm/h, and precipitation higher than 5 mm/h. Since the method is designed to work over different surface types, it relies mainly on the scattering signal of precipitation-sized ice particles received at high frequencies. For the calibration and validation of the method we use an eight-month dataset of combined weather radar and AMSU data obtained over the Baltic area. We compare results for the AMSU-B channels at 89 GHz and 150 GHz and find that the high frequency channel at 150 GHz allows for a much better discrimination of different types of precipitation than the 89 GHz channel. While precipitation-free areas, as well as heavily precipitating areas (> 5 mm/h), can be identified to high accuracy, the intermediate classes are more ambiguous. This stems from the ambiguity of the passive microwave observations as well as from the non-perfect matching of the different data sources and sub-optimal radar adjustment. In addition to a statistical assessment of the method's accuracy, we present case studies to demonstrate its capabilities to classify different types of precipitation and to work over highly structured, inhomogeneous surfaces.

We describe a method to remotely sense precipitation and classify its intensity over water, coast, and land surfaces. This method is intended to be used in a nowcasting environment. It is based on data obtained from the Advanced Microwave Sounding Unit (AMSU) onboard NOAA-15. Each observation is assigned a probability to belong to four different classes namely precipitation- free, risk of precipitation, precipitation between 0.5 and 5 mm/h and precipitation higher than 5 mm/h. Since the method is designed to work over different surface types, it mainly relies on the scatteringsignal of precipitation-sized ice particles received at high frequencies.

For the calibration and validation of the method we use an eight month dataset of combined radar and AMSU-data obtained over the Baltic area. We campare results for the AMSU-B channels at 89 GHz and 150 GHz and find that the high frequency channel at 150 GHz allows for a much better discrimination of different types of precipitation than the 89 GHz channel. While precipitation-free areas as well as heavily precipitating areas (> 5mm/h) can be identified to a high accuracy, the intennediate classes are more ambiguous. This ambiguity stems from the ambiguity of the passive microwave observations as well as from the non-perfect matching of the different data sources and non-perfect radar adjustment. In addition to a statistical assessment of the method's accuracy, we present case studies to demonstrate its capabilities to classify different types of precipitation and to seemlessly work over highly structured, inhomogeneous surfaces.

Statistical and balance features of forecast errors are generally incorporated in the background constraint of variational data assimilation. Forecast error covariances are here estimated with a spectral approach and from a set of forecast differences; autocovariances are calculated with a nonseparable scheme, and multiple linear regressions are used in the formulation of cross covariances. Such an approach was first developed for global models; it is here adapted to ALADIN, a bi-Fourier high-resolution limited-area model, and extended to a multivariate study of humidity forecast errors. Results for autocovariances confirm the importance of nonseparability, in terms of both vertical variability of horizontal correlations and dependence of vertical correlations with horizontal scale; high-resolution spatial correlations are obtained, which should enable a high-resolution analysis. Moreover nonnegligible relationships are found between forecast errors of humidity and those of mass and wind fields.

Observations made during late summer in the central Arctic Ocean, as part of the Arctic Summer Cloud Ocean Study (ASCOS), are used to evaluate cloud and vertical temperature structure in the Met Office Unified Model (MetUM). The observation period can be split into 5 regimes; the first two regimes had a large number of frontal systems, which were associated with deep cloud. During the remainder of the campaign a layer of low-level cloud occurred, typical of central Arctic summer conditions, along with two periods of greatly reduced cloud cover. The short-range operational NWP forecasts could not accurately reproduce the observed variations in near-surface temperature. A major source of this error was found to be the temperature-dependant surface albedo parameterisation scheme. The model reproduced the low-level cloud layer, though it was too thin, too shallow, and in a boundary-layer that was too frequently well-mixed. The model was also unable to reproduce the observed periods of reduced cloud cover, which were associated with very low cloud condensation nuclei (CCN) concentrations (< 1 cm(-3)). As with most global NWP models, the MetUM does not have a prognostic aerosol/cloud scheme but uses a constant CCN concentration of 100 cm(-3) over all marine environments. It is therefore unable to represent the low CCN number concentrations and the rapid variations in concentration frequently observed in the central Arctic during late summer. Experiments with a single-column model configuration of the MetUM show that reducing model CCN number concentrations to observed values reduces the amount of cloud, increases the near-surface stability, and improves the representation of both the surface radiation fluxes and the surface temperature. The model is shown to be sensitive to CCN only when number concentrations are less than 10-20 cm(-3).

An experimental comparison of spectral aerosol optical depth tau(a,lambda) derived from measurements by two spectral radiometers [a LI-COR, Inc., LI-1800 spectroradiometer and a Centre Suisse d'Electronique et de Microtechnique (CSEM) SPM2000 sun photometer] and a broadband field pyrheliometer has been made. The study was limited to three wavelengths ( 368, 500, and 778 nm), using operational calibration and optical depth calculation procedures. For measurements taken on 32 days spread over 1 yr, the rms difference in tau(a,lambda) derived from the two spectral radiometers was less than 0.01 at 500 and 778 nm. For wavelengths shorter than 500 nm and longer than 950 nm, the performance of the LI-1800 in its current configuration did not permit accurate determinations of tau(a,lambda). Estimates of spectral aerosol optical depth from broadband pyrheliometer measurements using two models of the Angstromngstrom turbidity coefficient were examined. For the broadband method that was closest to the sun photometer results, the mean (rms) differences in tau(a,lambda) were 0.014 (0.028), 0.014 (0.019), and 0.013 ( 0.014) at 368, 500, and 778 nm. The mean differences are just above the average uncertainties of the sun photometer tau(a,lambda) values (0.012, 0.011, and 0.011) for the same wavelengths, as determined through a detailed uncertainty analysis. The amount of atmospheric water vapor is a necessary input to the broadband methods. If upper-air sounding data are not available, water vapor from a meteorological forecast model yields significantly better turbidity results than does using estimates from surface measurements of air temperature and relative humidity.

[ 1] The Aerosol Optical Depths (AODs) retrieved from Brewer Ozone Spectrophotometer measurements with a method previously developed (Cheymol and De Backer, 2003) are now validated by comparisons between AODs from six Brewer spectrophotometers and two CSEM SPM2000 sunphotometers: two Brewer spectrophotometers 016 and 178 at Uccle in Belgium; one Brewer spectrophotometer 128 and one sunphotometer CSEM SPM2000 at Norrkoping in Sweden; and three Brewer instruments 040, 072, 156 at Arosa and one CSEM SPM2000 sunphotometer at Davos in Switzerland. The comparison between AODs from Brewer spectrophotometer 128 at 320.1 nm and sunphotometer SPM2000 at 368 nm at Norrkoping shows that the AODs obtained from the Brewer measurements with the Langley Plot Method (LPM) are very accurate if the neutral density filter spectral transmittances are well known: with the measured values of these filters, the correlation coefficient, the slope, and the intercept of the regression line are 0.98, 0.85 +/- 0.004, and 0.02 +/- 0.0014, respectively. The bias observed is mainly owing to the wavelength difference between the two instruments. The comparison between AODs from different Brewer spectrophotometers confirm that AODs will be in very good agreement if they are measured with several Brewer instruments at the same place: At Uccle, the correlation coefficient, slope, and intercept of the regression line are 0.98, 1.02 +/- 0.003, and 0.06 +/- 0.001, respectively; at Arosa, the comparisons between the AODs from three Brewer spectrophotometers 040, 072, and 156 give a correlation coefficient, a slope, and an intercept of the regression line above 0.94, 0.98 and below 0.04, respectively.

Radar services are occasionally affected by wind farms. This paper presents a comprehensive description of the effects that a wind farm may cause on the different radar services, and it compiles a review of the recent research results regarding the mitigation techniques to minimize this impact. Mitigation techniques to be applied at the wind farm and on the radar systems are described. The development of thorough impact studies before the wind farm is installed is presented as the best way to analyze in advance the potential for interference, and subsequently identify the possible solutions to allow the coexistence of wind farms and radar services.

The impact of very deep convection on the water budget and thermal structure of the tropical tropopause layer is still not well quantified, not least because of limitations imposed by the available observation techniques. Here, we present detailed analysis of the climatology of the cloud top brightness temperatures as indicators of deep convection during the Indian summer monsoon, and the variations therein due to active and break periods. We make use of the recently newly processed data from the Advanced Very High Resolution Radiometer (AVHRR) at a nominal spatial resolution of 4 km. Using temperature thresholds from the Atmospheric Infrared Sounder (AIRS), the AVHRR brightness temperatures are converted to climatological mean (2003-2008) maps of cloud amounts at 200, 150 and 100 hPa. Further, we relate the brightness temperatures to the level of zero radiative heating, which may allow a coarse identification of convective detrainment that will subsequently ascend into the stratosphere. The AVHRR data for the period 1982-2006 are used to document the differences in deep convection between active and break conditions of the monsoon. The analysis of AVHRR data is complemented with cloud top pressure and optical depth statistics (for the period 2003-2008) from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Aqua satellite. Generally, the two sensors provide a very similar description of deep convective clouds. Our analysis shows that most of the deep convection occurs over the Bay of Bengal and central northeast India. Very deep convection over the Tibetan plateau is comparatively weak, and may play only a secondary role in troposphere-to-stratosphere transport. The deep convection over the Indian monsoon region is most frequent in July/August, but the very highest convection (coldest tops, penetrating well into the TTL) occurs in May/June. Large variability in convection reaching the TTL is due to monsoon break/active periods. During the monsoon break period, deep convection reaching the TTL is almost entirely absent in the western part of the study area (i.e. 60 E-75 E), while the distribution over the Bay of Bengal and the Tibetan Plateau is less affected. Although the active conditions occur less frequently than the break conditions, they may have a larger bearing on the composition of the TTL within the monsoonal anticyclone, and tracer transport into the stratosphere because of deep convection occurring over anthropogenically more polluted regions.

A daytime climatological spatio-temporal distribution of high opaque ice cloud (HOIC) classes over the Indian subcontinent (0-40 degrees N, 60 degrees E-100 degrees E) is presented using 25-year data from the Advanced Very High Resolution Radiometers (AVHRRs) for the summer monsoon months. The HOICs are important for regional radiative balance, precipitation and troposphere-stratosphere exchange. In this study, HOICs are sub-divided into three classes based on their cloud top brightness temperatures (BT). Class I represents very deep convection (BT < 220 K). Class II represents deep convection (220 K <=BT < 233 K) and Class III background convection (233 K <=BT < 253 K). Apart from presenting finest spatial resolution (0.1x0.1 degrees) and long-term climatology of such cloud classes from AVHRRs to date, this study for the first time illustrates on (1) how these three cloud classes are climatologically distributed during monsoon months, and (2) how their distribution changes during active and break monsoon conditions. It is also investigated that how many deep convective clouds reach the tropopause layer during individual monsoon months. It is seen that Class I and Class II clouds dominate the Indian subcontinent during monsoon. The movement of monsoon over continent is very well reflected in these cloud classes. During monsoon breaks strong suppression of convective activity is observed over the Arabian Sea and the western coast of India. On the other hand, the presence of such convective activity is crucial for active monsoon conditions and all-India rainfall. It is found that a significant fraction of HOICs (3-5%) reach the tropopause layer over the Bay of Bengal during June and over the north and northeast India during July and August. Many cases are observed when clouds penetrate the tropopause layer and reach the lower stratosphere. Such cases mostly occur during June compared to the other months.

Data from the National Oceanic and Atmospheric Administration (NOAA) satellites' Advanced Very High Resolution Radiometers (AVHRRs) represent the longest record (more than 25 years) of continuously available satellite-based thermal measurements, and have well-chosen spatial and spectral resolutions. As a consequence, these data are used extensively to develop cloud climatologies. However, for such applications, accurate calibration and intercalibration of both solar and thermal channels of the AVHRRs is necessary so as to homogenize the data obtained from the different AVHRR sensors. AVHRR thermal channels 4 and 5 are routinely used in threshold-based hierarchical decision-tree cloud detection and classification algorithms, and therefore an evaluation of the stability of these channels at low temperatures is important. In this letter, the AVHRR channel 4 and 5 brightness temperatures (BTs) are compared at five stations in Antarctica. The data for the period of June, July and August (the coldest months of every year and with minimal atmospheric influence) from 1982 to 2006 were used for the evaluations. The calibration and intercalibration of the thermal channels are found to be very robust. The root mean square errors (RMSEs) range from 2.2 to 3.4K and the correlation coefficients from 0.84 to 0.95. No apparent artefacts or artificial jumps in the BTs are visible in the data series after changes of sensors. The BTs from the thermal channels of the AVHRRs can be used for preparing cloud climatologies, as their intercalibration is found to be consistent across different afternoon satellites.

The Advanced Very High Resolution Radiometer (AVHRR) instruments onboard the series of National Oceanic and Atmospheric Administration (NOAA) satellites offer the longest available meteorological data records from space. These satellites have drifted in orbit resulting in shifts in the local time sampling during the life span of the sensors onboard. Depending upon the amplitude of the diurnal cycle of the geophysical parameters derived, orbital drift may cause spurious trends in their time series. We investigate tropical deep convective clouds, which show pronounced diurnal cycle amplitude, to estimate an upper bound of the impact of orbital drift on their time series. We carry out a rotated empirical orthogonal function analysis (REOF) and show that the REOFs are useful in delineating orbital drift signal and, more importantly, in subtracting this signal in the time series of convective cloud amount. These results will help facilitate the derivation of homogenized data series of cloud amount from NOAA satellite sensors and ultimately analyzing trends from them. However, we suggest detailed comparison of various methods and rigorous testing thereof applying final orbital drift corrections.

Using measurements from the national network of 12 weather radar stations for the 11-year period 2000-2010, we investigate the large-scale spatio-temporal variability of precipitation over Sweden. These statistics provide useful information to evaluate regional climate models as well as for hydrology and energy applications. A strict quality control is applied to filter out noise and artifacts from the radar data. We focus on investigating four distinct aspects: the diurnal cycle of precipitation and its seasonality, the dominant timescale (diurnal versus seasonal) of variability, precipitation response to different wind directions, and the correlation of precipitation events with the North Atlantic Oscillation (NAO) and the Arctic Oscillation (AO). When classified based on their intensity, moderate-to high-intensity events (precipitation >0.34 mm/3 h) peak distinctly during late afternoon over the majority of radar stations in summer and during late night or early morning in winter. Precipitation variability is highest over the southwestern parts of Sweden. It is shown that the high-intensity events (precipitation >1.7 mm/3 h) are positively correlated with NAO and AO (esp. over northern Sweden), while the low intensity events are negatively correlated (esp. over southeastern parts). It is further observed that southeasterly winds often lead to intense precipitation events over central and northern Sweden, while southwesterly winds contribute most to the total accumulated precipitation for all radar stations. Apart from its operational applications, the present study demonstrates the potential of the weather radar data set for studying climatic features of precipitation over Sweden.

The record sea ice minimum (SIM) extents observed during the summers of 2007 and 2012 in the Arctic are stark evidence of accelerated sea ice loss during the last decade. Improving our understanding of the Arctic atmosphere and accurate quantification of its characteristics becomes ever more crucial, not least to improve predictions of such extreme events in the future. In this context, the Atmospheric Infrared Sounder (AIRS) instrument onboard NASA's Aqua satellite provides crucial insights due to its ability to provide 3-D information on atmospheric thermodynamics. Here, we facilitate comparisons in the evolution of the thermodynamic state of the Arctic atmosphere during these two SIM events using a decade-long AIRS observational record (2003-2012). It is shown that the meteorological conditions during 2012 were not extreme, but three factors of preconditioning from winter through early summer played an important role in accelerating sea ice melt. First, the marginal sea ice zones along the central Eurasian and North Atlantic sectors remained warm throughout winter and early spring in 2012 preventing thicker ice build-up. Second, the circulation pattern favoured efficient sea ice transport out of the Arctic in the Atlantic sector during late spring and early summer in 2012 compared to 2007. Third, additional warming over the Canadian archipelago and southeast Beaufort Sea from May onward further contributed to accelerated sea ice melt. All these factors may have lead the already thin and declining sea ice cover to pass below the previous sea ice extent minimum of 2007. In sharp contrast to 2007, negative surface temperature anomalies and increased cloudiness were observed over the East Siberian and Chukchi seas in the summer of 2012. The results suggest that satellite-based monitoring of atmospheric preconditioning could be a critical source of information in predicting extreme sea ice melting events in the Arctic.

An accurate characterization of the vertical structure of the Arctic atmosphere is useful in climate change and attribution studies as well as for the climate modelling community to improve projections of future climate over this highly sensitive region. Here, we investigate one of the dominant features of the vertical structure of the Arctic atmosphere, i.e. water-vapour inversions, using eight years of Atmospheric Infrared Sounder data (2002-2010) and radiosounding profiles released from the two Arctic locations (North Slope of Alaska at Barrow and during SHEBA). We quantify the characteristics of clear-sky water vapour inversions in terms of their frequency of occurrence, strength and height covering the entire Arctic for the first time. We found that the frequency of occurrence of water-vapour inversions is highest during winter and lowest during summer. The inversion strength is, however, higher during summer. The observed peaks in the median inversion-layer heights are higher during the winter half of the year, at around 850 hPa over most of the Arctic Ocean, Siberia and the Canadian Archipelago, while being around 925 hPa during most of the summer half of the year over the Arctic Ocean. The radiosounding profiles agree with the frequency, location and strength of water-vapour inversions in the Pacific sector of the Arctic. In addition, the radiosoundings indicate that multiple inversions are the norm with relatively few cases without inversions. The amount of precipitable water within the water-vapour inversion structures is estimated and we find a distinct, two-mode contribution to the total column precipitable water. These results suggest that water-vapour inversions are a significant source to the column thermodynamics, especially during the colder winter and spring seasons. We argue that these inversions are a robust metric to test the reproducibility of thermodynamics within climate models. An accurate statistical representation of water-vapour inversions in models would mean that the large-scale coupling of moisture transport, precipitation, temperature and water-vapour vertical structure and radiation are essentially captured well in such models.

Simulating the radiative impacts of aerosols located above liquid water clouds presents a significant challenge. In particular, absorbing aerosols, such as smoke, may have significant impact in such situations and even change the sign of net radiative forcing. It is not possible to reliably obtain information on such overlap events from existing passive satellite sensors. However, the CALIOP instrument onboard NASA's CALIPSO satellite allows us to examine these events with unprecedented accuracy. Using four years of collocated CALIPSO 5 km Aerosol and Cloud Layer Version 3 Products (June 2006 May 2010), we quantify, for the first time, the characteristics of overlapping aerosol and water cloud layers globally. We investigate seasonal variability in these characteristics over six latitude bands to understand the hemispheric differences when all aerosol types are included in the analysis (the AAO case). We also investigate frequency of smoke aerosol-cloud overlap (the SAO case). Globally, the frequency is highest during the JJA months in the AAO case, while for the SAO case, it is highest in the SON months. The seasonal mean overlap frequency can regionally exceed 20% in the AAO case and 10% in the SAO case. In about 5-10% cases the vertical distance between aerosol and cloud layers is less than 100 m, while about in 45-60% cases it less than a kilometer in the annual means for different latitudinal bands. In about 70-80% cases, aerosol layers are less than a kilometer thick, while in about 18-22% cases they are 1-2 km thick. The frequency of aerosol layers 2-3 km thick is about 4-5% in the tropical belts during overlap events. Over the regions where high aerosol loadings are present, the overlap frequency can be up to 50% higher when quality criteria on aerosol/cloud feature detection are relaxed. Over the polar regions, more than 50% of the overlapping aerosol layers have optical thickness less than 0.02, but the contribution from the relatively optically thicker aerosol layers increases towards the equatorial regions in both hemispheres. The results suggest that the frequency of occurrence of overlap events is far from being negligible globally.

Temperature inversions influence the local air quality at smaller scales and the pollution transport at larger spatio-temporal scales and are one of the most commonly observed meteorological phenomena over Scandinavia (54 degrees N-70 degrees N, 0-30 degrees E) during winter. Here, apart from presenting key statistics on temperature inversions, a large-scale co-variation of inversion strength and carbon monoxide (CO), an ideal pollution tracer, is further quantified at six vertical levels in the free troposphere during three distinct meteorological regimes that are identified based on inversion strength. Collocated temperature and CO profiles from Atmospheric Infrared Sounder (AIRS) are used for this purpose. Higher values of CO (up to 15%) are observed over Scandinavia during weakly stable regimes at all vertical levels studied, whereas lower CO values (up to 10%) are observed when inversions become stronger and elevated. The observed systematic co-variation between CO and inversion strength in three meteorological regimes is most likely explained by the efficacy of long-range transport to influence tropospheric composition over Scandinavia. We argue that this large-scale co-variation of temperature inversions and CO would be a robust metric to test coupling of large-scale meteorology and chemistry in transport models. (C) 2012 Elsevier Ltd. All rights reserved.

The main purpose of this study is to underline the sensitivity of cloud liquid water content (LWC) estimates purely to 1) the shape of computationally simplified temperature-dependent thermodynamic phase and 2) the range of subzero temperatures covered to partition total cloud condensate into liquid and ice fractions. Linear, quadratic, or sigmoid-shaped functions for subfreezing temperatures (down to -20 degrees or -40 degrees C) are often used in climate models and reanalysis datasets for partitioning total condensate. The global vertical profiles of clouds obtained from CloudSat for the 4-yr period June 2006-May 2010 are used for sensitivity analysis and the quantitative estimates of sensitivities based on these realistic cloud profiles are provided. It is found that three cloud regimes in particular-convective clouds in the tropics, low-level clouds in the northern high latitudes, and middle-level clouds over the midlatitudes and Southern Ocean-are most sensitive to assumptions on thermodynamic phase. In these clouds, the LWC estimates based purely on quadratic or sigmoid-shaped functions with a temperature range down to -20 degrees C can differ by up to 20%-40% over the tropics (in seasonal means). 10%-30% over the midlatitudes, and up to 50% over high latitudes compared to a linear assumption. When the temperature range is extended down to -40 degrees C. LWC estimates in the sigmoid case can be much higher than the above values over high-latitude regions compared to the commonly used case with quadratic dependency down to -20 C. This sensitivity study emphasizes the need to critically investigate radiative impacts of cloud thermodynamic phase assumptions in simplified climate models and reanalysis datasets.

The main purpose of this study is to investigate the influence of the Arctic Oscillation (AO), the dominant mode of natural variability over the northerly high latitudes, on the spatial (horizontal and vertical) distribution of clouds in the Arctic. To that end, we use a suite of sensors on-board NASA's A-Train satellites that provide accurate observations of the distribution of clouds along with information on atmospheric thermodynamics. Data from three independent sensors are used (AQUA-AIRS, CALIOP-CALIPSO and CPR-CloudSat) covering two time periods (winter half years, November through March, of 2002-2011 and 2006-2011, respectively) along with data from the ERA-Interim reanalysis. We show that the zonal vertical distribution of cloud fraction anomalies averaged over 67-82 degrees N to a first approximation follows a dipole structure (referred to as "Greenland cloud dipole anomaly", GCDA), such that during the positive phase of the AO, positive and negative cloud anomalies are observed eastwards and westward of Greenland respectively, while the opposite is true for the negative phase of AO. By investigating the concurrent meteorological conditions (temperature, humidity and winds), we show that differences in the meridional energy and moisture transport during the positive and negative phases of the AO and the associated thermodynamics are responsible for the conditions that are conducive for the formation of this dipole structure. All three satellite sensors broadly observe this large-scale GCDA despite differences in their sensitivities, spatio-temporal and vertical resolutions, and the available lengths of data records, indicating the robustness of the results. The present study also provides a compelling case to carry out process-based evaluation of global and regional climate models.

Clouds play a crucial role in the Arctic climate system. Therefore, it is essential to accurately and reliably quantify and understand cloud properties over the Arctic. It is also important to monitor and attribute changes in Arctic clouds. Here, we exploit the capability of the CALIPSO-CALIOP instrument and provide comprehensive statistics of tropospheric thin clouds, otherwise extremely difficult to monitor from passive satellite sensors. We use 4 yr of data (June 2006-May 2010) over the circumpolar Arctic, here defined as 67-82 degrees N, and characterize probability density functions of cloud base and top heights, geometrical thickness and zonal distribution of such cloud layers, separately for water and ice phases, and discuss seasonal variability of these properties. When computed for the entire study area, probability density functions of cloud base and top heights and geometrical thickness peak at 200-400, 1000-2000 and 400-800 m, respectively, for thin water clouds, while for ice clouds they peak at 6-8, 7-9 and 400-1000 m, respectively. In general, liquid clouds were often identified below 2 km during all seasons, whereas ice clouds were sensed throughout the majority of the upper troposphere and also, but to a smaller extent, below 2 km for all seasons.

Influx of aerosols from the mid-latitudes has a wide range of impacts on the Arctic atmosphere. In this study, the capability of the CALIPSO-CALIOP instrument to provide accurate observations of aerosol layers is exploited to characterize their vertical distribution, probability density functions (PDFs) of aerosol layer thickness, base and top heights, and optical depths over the Arctic for the 4-yr period from June 2006 to May 2010. It is shown that the bulk of aerosols, from about 65% in winter to 45% in summer, are confined below the lowermost kilometer of the troposphere. In the middle troposphere (3-5 km), spring and autumn seasons show slightly higher aerosol amounts compared to other two seasons. The relative vertical distribution of aerosols shows that clean continental aerosol is the largest contributor in all seasons except in summer, when layers of polluted continental aerosols are almost as large. In winter and spring, polluted continental aerosols are the second largest contributor to the total number of observed aerosol layers, whereas clean marine aerosol is the second largest contributor in summer and autumn. The PDFs of the geometrical thickness of the observed aerosol layers peak about 400-700 m. Polluted continental and smoke aerosols, which are associated with the intrusions from mid-latitudes, have much broader distributions of optical and geometrical thicknesses, suggesting that they appear more often optically thicker and higher up in the troposphere.

Temperature inversions are one of the dominant features of the Arctic atmosphere and play a crucial role in various processes by controlling the transfer of mass and moisture fluxes through the lower troposphere. It is therefore essential that they are accurately quantified, monitored and simulated as realistically as possible over the Arctic regions. In the present study, the characteristics of inversions in terms of frequency and strength are quantified for the entire Arctic Ocean for summer and winter seasons of 2003 to 2008 using the AIRS data for the clear-sky conditions. The probability density functions (PDFs) of the inversion strength are also presented for every summer and winter month. Our analysis shows that although the inversion frequency along the coastal regions of Arctic decreases from June to August, inversions are still seen in almost each profile retrieved over the inner Arctic region. In winter, inversions are ubiquitous and are also present in every profile analysed over the inner Arctic region. When averaged over the entire study area (70 degrees N-90 degrees N), the inversion frequency in summer ranges from 69 to 86% for the ascending passes and 72-86% for the descending passes. For winter, the frequency values are 88-91% for the ascending passes and 89-92% for the descending passes of AIRS/AQUA. The PDFs of inversion strength for the summer months are narrow and right-skewed (or positively skewed), while in winter, they are much broader. In summer months, the mean values of inversion strength for the entire study area range from 2.5 to 3.9 K, while in winter, they range from 7.8 to 8.9 K. The standard deviation of the inversion strength is double in winter compared to summer. The inversions in the summer months of 2007 were very strong compared to other years. The warming in the troposphere of about 1.5-3.0K vertically extending up to 400 hPa was observed in the summer months of 2007.

New methods and software for cloud detection and classification at high and midlatitudes using Advanced Very High Resolution Radiometer (AVHRR) data are developed for use in a wide range of meteorological, climatological, land surface, and oceanic applications within the Satellite Application Facilities (SAFs) of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), including the SAF for Nowcasting and Very Short Range Forecasting Applications (NWCSAF) project. The cloud mask employs smoothly varying (dynamic) thresholds that separate fully cloudy or cloud-contaminated fields of view from cloud-free conditions. Thresholds are adapted to the actual state of the atmosphere and surface and the sun-satellite viewing geometry using cloud-free radiative transfer model simulations. Both the cloud masking and the cloud-type classification are done using sequences of grouped threshold tests that employ both spectral and textural features. The cloud-type classification divides the cloudy pixels into 10 different categories: 5 opaque cloud types, 4 semitransparent clouds, and 1 subpixel cloud category. The threshold method is fuzzy in the sense that the distances in feature space to the thresholds are stored and are used to determine whether to stop or to continue testing. They are also used as a quality indicator of the final output. The atmospheric state should preferably be taken from a short-range NWP model, but the algorithms can also run with climatological fields as input.

Algorithms for cloud detection (cloud mask) and classification (cloud type) at high and midlatitudes using data from the Advanced Very High Resolution Radiometer (AVHRR) on board the current NOAA satellites and future polar Meteorological and Operational Weather Satellites (METOP) of the European Organisation for the Exploitation of Meteorological Satellites have been extensively validated over northern Europe and the adjacent seas. The algorithms have been described in detail in Part I and are based on a multispectral grouped threshold approach, making use of cloud-free radiative transfer model simulations. The thresholds applied in the algorithms have been validated and tuned using a database interactively built up over more than 1 yr of data from NOAA-12, -14, and -15 by experienced nephanalysts. The database contains almost 4000 rectangular (in the image data)-sized targets (typically with sides around 10 pixels), with satellite data collocated in time and space with atmospheric data from a short-range NWP forecast model, land cover characterization, elevation data, and a label identifying the given cloud or surface type as interpreted by the nephanalyst. For independent and objective validation, a large dataset of nearly 3 yr of collocated surface synoptic observation (Synop) reports, AVHRR data, and NWP model output over northern and central Europe have been collected. Furthermore, weather radar data were used to check the consistency of the cloud type. The cloud mask performs best over daytime sea and worst at twilight and night over land. As compared with Synop, the cloud cover is overestimated during night (except for completely overcast situations) and is underestimated at twilight. The algorithms have been compared with the more empirically based Swedish Meteorological and Hydrological Institute (SMHI) Cloud Analysis Model Using Digital AVHRR Data (SCANDIA), operationally run at SMHI since 1989, and results show that performance has improved significantly.

A new Polar Platform Systems (PPS) Cloud Mask (CM) test sequence is required for improving cloud detection during Arctic winter conditions. This study introduces a test sequence, called Ice Night Sea (INS), that to a greater extent successfully detects clouds over ice surfaces and which is less sensitive to cloud free misclassification.The test sequence uses a combination of Numerical Weather Prediction (NWP) fields and Advanced Very High Resolution Radiometer (AVHRR) satellite data. Only the infrared (IR) AVHRR channels can be exploited during night conditions. Training target data from winter 2001-2002, collected over a large area north of the Atmospheric Radiation Measurement (ARM) site at Barrow, Alaska, were used to assess the general atmospheric state of the Arctic and to perform a qualitative validation of CM test sequences. Results clearly show that the atmospheric conditions during Arctic winter severely hamper cloud detection efforts. Very cold surface temperatures and immense surface temperature inversions lead to a diminished separability between surfaces and clouds. One particular problem is that the IR brightness temperatures for the shortest wavelength (3.7μm - henceforth T37) are strongly affected by noise. The use of an IR noise filter was shown to improve results significantly. In addition, the problem of misclassifying cracks in the pack ice as Cirrus clouds was basically solved by using a dedicated filter using the local variance of T37.Using an inverse version of a typical daytime Cirrus test (based on just two IR channels and normally applied successfully outside the Arctic region), it is shown that we can detect a substantial part of the warmsemi-transparent clouds commonly found in the Arctic. Running the test sequences on training target data revealed an improvement in correct cloud free target classification of around 30% but only a marginal improvement for cloudy training targets. However, visual inspection of results obtained for about 50 scenes covering a large part of the Arctic region in January 2007 clearly indicated improvements also for the cloudy portion of the scenes. The INS CM test sequence awaits a more rigorous and quantitative validation, e.g. based on comparisons with CLOUDSAT/CALIPSO satellite data sets.