Specific Humidity Profiles Research Articles

Combining low- and high-frequency microwave radiometer measurements from the MOSAiC expedition for enhanced water vapour products

Abstract. In the central Arctic, high-quality water vapour observations are sparse due to the low density of meteorological stations and uncertainties in satellite remote sensing. Different reanalyses also disagree on the amount of water vapour in the central Arctic. The Multidisciplinary drifting Observatory for the Study of the Arctic Climate (MOSAiC) expedition provides comprehensive observations that are suitable for evaluating satellite products and reanalyses. Radiosonde observations provide high-quality water vapour estimates with a high vertical but a low temporal resolution. Observations from the microwave radiometers (MWRs) on board the research vessel Polarstern complement these observations through high temporal resolution. In this study, we demonstrate the high accuracy of the combination of the two MWRs HATPRO (Humidity and Temperature Profiler) and MiRAC-P (Microwave Radiometer for Arctic Clouds – Passive). For this purpose, we developed new retrievals of integrated water vapour (IWV) and profiles of specific humidity and temperature using a neural network approach, including observations from both HATPRO and MiRAC-P to utilize their different water vapour sensitivity. The retrievals were trained with the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis version 5 (ERA5) and synthetic MWR observations simulated with the Passive and Active Microwave radiative TRAnsfer tool (PAMTRA). We applied the retrievals to synthetic and real observations and evaluated them with ERA5 and radiosondes launched during MOSAiC, respectively. To assess the benefit of the combination of HATPRO and MiRAC-P compared to single MWR retrievals, we compared the errors with respect to MOSAiC radiosondes and computed the vertical information content of the specific humidity profiles. The root mean squared error (RMSE) of IWV was reduced by up to 15 %. Specific humidity biases and RMSE were reduced by up to 75 % and 50 %, respectively. The vertical information content of specific humidity could be increased from 1.7 to 2.4 degrees of freedom. We also computed relative humidity from the retrieved temperature and specific humidity profiles and found that RMSE was reduced from 45 % to 15 %. Finally, we show a case study demonstrating the enhanced humidity profiling capabilities compared to the standard HATPRO-based retrievals. The vertical resolution of the retrieved specific humidity profiles is still low compared to radiosondes, but the case study revealed the potential to resolve major humidity inversions. To what degree the MWR combination detects humidity inversions, also compared to satellites and reanalyses, will be part of future work.

УСВОЕНИЕ ДАННЫХ РАДИОЗАТМЕННЫХ НАБЛЮДЕНИЙ В ЧИСЛЕННОМ ПРОГНОЗЕ ПОГОДЫ

The paper discusses the principles of the assimilation of radio occultation observations into numerical weather prediction models. The most widely used approach is assimilation of the bending angle profiles obtained from the observations by the wave optics methods. The observation operators are constructed from the geometric optics equations and can be three-dimensional (3D), taking into account the actual field of meteorological parameters, or one-dimensional (1D), using only one vertical profile. In the approximation of weak gradients, the 3D operator is reduced to the two-dimensional (2D) one, which takes into account the section of the fields of meteorological parameters by the vertical plane of the occultation event. Another approach is the assimilation of the retrieved atmospheric refractivity profiles by means of a simplest 1D or more accurate 2D operator. Some examples of statistical comparisons of the profiles of temperature, pressure, and specific humidity obtained by variational assimilation with the GFS reanalysis data are given.

Initial evaluation of humidity profiles retrieved by the EOS-07 millimetre-wave humidity sounder

ABSTRACT The vertical profiles of specific humidity in the troposphere measured by the millimetre-wave humidity sounder (MHS) onboard of the Earth Observation Satellite (EOS)-07 (Microsoft 2B) are validated using ground-based radiosonde observations during July-August 2023. MHS onboard EOS-07 (hereafter referred as EOS-07/MHS) is a 6-channel cross-track scanning radiometer operating in the 183.31 ± 16.25 GHz band. The EOS-07 observations are used to retrieve three-dimensional specific humidity profiling from 1000 hPa (surface) to 100 hPa (16 km) atmospheric pressure levels with a spatial resolution of 10 km at nadir. The EOS-07/MHS measurements over India have a high correlation coefficient of 0.98 with the radiosonde observations. The mean relative bias is found to be 0.44 ± 0.55 g kg−1 below 350hPa and 2.8 ± 2.3 g kg−1 above 350 hPa. It is also found that above the 350 hPa level, MHS seems to be systematically overestimating the radiosonde measurements. After that, the EOS-07/MHS measurements are used to construct the diurnal variation of humidity during the active phases of the Indian summer monsoon. The observations are consistent with the present understanding of the Indian summer monsoon system. The present results are very encouraging and demonstrate the great potential of EOS-07/MHS observations of humidity for meteorological application, especially in understanding the hydrological cycle.

Quality Assessment of the Atmospheric Radio Occultation Profiles from FY-3E/GNOS-II BDS and GPS Measurements

The Fengyun-3E (FY-3E) satellite carrying the advanced Global Navigation Satellite System (GNSS) Radio Occultation Sounder-II (GNOS-II) is already in operation for radio occultation (RO) observation, with the BeiDou Navigation Satellite System (BDS-2 and BDS-3) and Global Positioning System (GPS) signals tracking capability. FY-3E BDS and GPS RO signals tracking capability were first evaluated by comparing their penetration depths, and then the quality of the refractivity, temperature, and specific humidity profiles was analyzed with the fifth-generation European Centre for Medium-Range Weather Forecasts reanalysis (ERA5) data. Results show the mean penetration depth of BDS occultations was 1.65 km compared to 1.09 km of GPS occultations. Between 5 and 25 km, the mean refractivity bias of the BDS (GPS) was −0.14% (0.01%) with the mean standard deviation (SD) being 1.11% (1.52%); the mean temperature biases of both were within ±0.1 K, and the mean SD of BDS was 1.1 K compared to 1.2 K for the GPS; BDS/GPS specific humidity bias was within ±0.3 g/kg with corresponding SD being less than 1.3 g/kg. Seasonal deviations of specific humidities were largest in summer and smallest in winter. Latitudinal deviations over the tropics were generally higher than in other areas. Enriched quantity and high accuracy and precision after careful calibration will promote the FY-3E RO profiles as a reliable data source for the RO community.

Improving the Reliability of ML‐Corrected Climate Models With Novelty Detection

AbstractUsing machine learning (ML) for the online correction of coarse‐resolution atmospheric models has proven effective in reducing biases in near‐surface temperature and precipitation rate. However, ML corrections often introduce new biases in the upper atmosphere and causes inconsistent model performance across different random seeds. Furthermore, they produce profiles that are outside the distribution of samples used in training, which can interfere with the baseline physics of the atmospheric model and reduce model reliability. This study introduces the use of a novelty detector to mask ML corrections when the atmospheric state is deemed out‐of‐sample. The novelty detector is trained on profiles of temperature and specific humidity in a semi‐supervised fashion using samples from the coarsened reference fine‐resolution simulation. The novelty detector responds to particularly biased simulations relative to the reference simulation by categorizing more columns as out‐of‐sample. Without novelty detection, corrective ML occasionally causes undesirably large climate biases. When coupled to a running year‐long coarse‐grid simulation, novelty detection deems about 21% of columns to be novelties. This identification reduces the spread in the root‐mean‐square error (RMSE) of time‐mean spatial patterns of surface temperature and precipitation rate across a random seed ensemble. In particular, the random seed with the worst RMSE is improved by up to 60% (depending on the variable) while the best seed maintains its low RMSE. By reducing the variance in quality of ML‐corrected climate models, novelty detection offers reliability without compromising prediction quality in atmospheric models.

Comparison of pressure, temperature and specific humidity from COSMIC-2 with radiosonde and ERA5

ABSTRACT Pressure, temperature and specific humidity profiles from COSMIC-2 are compared with those from ERA5 and radiosonde data from October 2019 to September 2020. The results demonstrate that COSMIC-2 pressure and specific humidity profiles show relatively poor accuracy and stability in the lower troposphere. The 1D-Var solution effectively improves the accuracy of temperature parameters in the troposphere. Three parameters show high accuracy over one climatological year, but show slight seasonal fluctuations, especially for the specific humidity profiles in summer. Furthermore, COSMIC-2 specific humidity data show low consistency with radiosonde and ERA5 in the equatorial region.

Observation of an Extremely Dry Atmospheric Air Column above Bern

The water vapour column density or vertically integrated water vapour (IWV) ranges from about 8 mm in winter to about 25 mm in summer in Bern, Switzerland. However, there can be day episodes when IWV drops to 2 mm or even less so that the atmosphere is extremely dry. We selected an event in February 2021 when the tropospheric water radiometer TROWARA measured a mean IWV value of about 1.5 ± 0.2 mm for a time interval of about one day in Bern. The ECMWF reanalysis ERA5 indicated a slightly higher IWV value of about 2.2 ± 0.4 mm where the uncertainty is the standard deviation of IWV during the time of IWV depression. The ERA5 profiles of relative humidity and specific humidity during this episode are reduced by 50% and more compared to the monthly mean profiles. On a global map, it can be seen that Bern is within a mesoscale dry region on that day with descending wind. Back trajectory analysis gives the result that the dry air masses in Bern came from the North and the trajectories are descending in altitude so that dry air from the mid troposphere came into the lower troposphere. These descending air masses from the North explain the minimum of IWV observed in Bern on 13–14 February 2021. The surface climate in Switzerland was dominated by a cold wave at that time. At the same time, severe cold waves occurred in Greece and Northern America.

Joint Use of Far‐Infrared and Mid‐Infrared Observation for Sounding Retrievals: Learning From the Past for Upcoming Far‐Infrared Missions

AbstractAtmosphere and surface properties are routinely retrieved from satellite measurements and extensively used in weather forecast and climate analysis. Satellite missions dedicated to fill the gap of far‐infrared (far‐IR) observations are scheduled to be launched this decade. To explore mid‐infrared (mid‐IR) and far‐IR joint retrievals for the future far‐IR satellite missions, this study uses an optimal‐estimation‐based method to retrieve atmospheric specific humidity and temperature profiles, surface skin temperature, and surface spectral emissivity from the Infrared Interferometer Sounder‐D (IRIS‐D) measurements in 1970, the only existing spaceborne far‐IR spectral observations with global coverage. Based on a set of criteria, two cases in the Arctic, which are most likely under clear‐sky conditions, are chosen for the retrieval experiments. Information content analysis suggests that the retrieved surface skin temperature and the mid‐IR surface spectral emissivity are highly sensitive to the true values while the retrieval estimates of far‐IR surface emissivity are subject to the variation of water vapor abundance. Results show that radiances based on the retrieved state variables are more consistent with the IRIS‐D observations compared to those based on the reanalysis data. Retrieval estimates of the state variables along with retrieval uncertainties generally fall within reasonable ranges. The relative uncertainties of retrieved state variables decrease compared to the a priori relative uncertainties. In addition, the necessity to retrieve surface emissivity is corroborated by a parallel retrieval experiment assuming a blackbody surface emissivity that has revealed significant distortions of retrieved specific humidity and temperature profiles in the Arctic lower troposphere.

Comparative Analysis of Binhu and Cosmic-2 Radio Occultation Data

Global Navigation Satellite System (GNSS) radio occultation (RO) technology has been widely used in Earth atmospheric detection and has a significant impact on numerical weather prediction (NWP), climate detection, and other fields. Cosmic-2 LEO-1 (C2E1) is a well-known RO data provider; however, its observations are confined to 45°S and 45°N. Recently, the Binhu meteorological observation test satellite (BH) has provided global coverage of RO data, including refractivity, specific humidity, and temperature data. In this study, RO data from BH and C2E1 are analyzed and compared from 8 February 2022 to 17 February 2022. Employing the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA-5) data as a reference, both BH and C2E1 RO data agree with the ERA data, with the refractivity, temperature, and specific humidity profiles reflecting the real conditions of the natural atmosphere. In addition, BH data are comparable to C2E1 data at low and middle latitudes (0–45°), and BH data at middle and high latitudes (45–90°) are of better quality than those at low and middle latitudes (0–45°). For example, without considering the errors introduced by the interpolation of the ERA-5 data for comparative analysis, the BH refractivity profiles show a mean absolute bias of 0.73 N at low and middle latitudes and only 0.23 N at middle and high latitudes, and that for BH specific humidity profiles at middle and high latitudes is 0.015 g/kg, which is only half of that at low and middle latitudes. The BH temperature and specific humidity data show promising data accuracy. Therefore, BH RO data may provide important supplementary data at higher latitudes and may improve future NWPs through assimilation.

An optimal estimation algorithm for the retrieval of fog and low cloud thermodynamic and micro-physical properties

Abstract. A new generation of cloud radars, with the ability to make observations close to the surface, presents the possibility of observing fog properties with better insight than was previously possible. The use of these instruments as part of an operational observation network could improve the prediction of fog events, something which is still a problem for even high-resolution numerical weather prediction models. However, the retrieval of liquid water content (LWC) profiles from radar reflectivity alone is an under-determined problem, something which ground-based microwave radiometer observations can help to constrain. In fact, microwave radiometers are not only sensitive to temperature and humidity profiles but are also known to be instruments of reference for the liquid water path. By providing the thermodynamic state of the atmosphere, to which the formation and evolution of fog events are highly sensitive, in addition to accurate liquid water path, which can be used to constrain the LWC retrieval from the cloud radar alone, combining microwave radiometers with cloud radars seems a natural next step to better understand and forecast fog events. To that end, a newly developed one-dimensional variational (1D-Var) algorithm designed for the retrieval of temperature, specific humidity and liquid water content profiles with both cloud radar and microwave radiometer (MWR) observations is presented in this study. The algorithm was developed to evaluate the capability of cloud radar and MWR to provide accurate LWC profiles in addition to temperature and humidity in view of assimilating the retrieved profiles into a 3D- and 4D-Var operational assimilation system. The algorithm is firstly tested on a synthetic dataset, which allows the evaluation of the developed algorithm in idealised conditions. This dataset was constructed by perturbing a high-resolution forecast dataset of fog and low-cloud cases by its expected errors. The algorithm is then tested with real data from the recent field campaign SOFOG-3D, carried out with the use of LWC measurements made from a tethered balloon platform. As expected, results from the synthetic dataset study were found to contain lower errors than those found from the retrievals on the dataset of real observations. It was found that LWC can be retrieved in idealised conditions with an uncertainty of less than 0.04 g m−3. With real data, as expected, retrievals with a good correlation (0.7) to in situ measurements were found but with a higher uncertainty than the synthetic dataset of around 0.06 g m−3 (41 %). This was reduced to 0.05 g m−3 (35 %) when an accurate droplet number concentration could be prescribed to the algorithm. A sensitivity study was conducted to discuss the impact of different settings used in the 1D-Var algorithm and the forward operator. Additionally, retrievals of LWC from a real fog event observed during the SOFOG-3D field campaign were found to significantly improve the operational background profiles of the AROME (Application of Research to Operations at MEsoscale) model, showing encouraging results for future improvement of the AROME model initial state during fog conditions.

A Comprehensive Clear-Sky Database for the Development of Land Surface Temperature Algorithms

Land surface temperature is linked to a wide range of surface processes. Given the increased development of earth observation systems, a large effort has been put into advancing land surface temperature retrieval algorithms from remote sensors. Due to the very limited number of reliable in situ observations matching the spatial scales of satellite observations, algorithm development relies on synthetic databases, which then constitute a crucial part of algorithm development. Here we provide a database of atmospheric profiles and respective surface conditions that can be used to train and verify algorithms for land surface temperature retrieval, including machine learning techniques. The database was built from ERA5 data resampled through a dissimilarity criterion applied to the temperature and specific humidity profiles. This criterion aims to obtain regular distributions of these variables, ensuring a good representation of all atmospheric conditions. The corresponding vertical profiles of ozone and relevant surface and vertically integrated variables are also included in the dataset. Information on the surface conditions (i.e., temperature and emissivity) was complemented with data from a wide array of satellite products, enabling a more realistic surface representation. The dataset is freely available online at Zenodo.

Winter thermodynamic vertical structure in the Arctic atmosphere linked to large-scale circulation

Abstract. Thermodynamic profiles are affected by both the large-scale dynamics and the local processes, such as radiation, cloud formation and turbulence. Based on ERA5 reanalysis, radiosoundings and cloud cover observations from winters 2009–2018, this study demonstrates manifold impacts of large-scale circulation on temperature and specific humidity profiles in the circumpolar Arctic north of 65∘ N. Characteristic wintertime circulation types are allocated using self-organizing maps (SOMs). The study shows that influence of different large-scale flows must be viewed as a progressing set of processes: (1) horizontal advection of heat and moisture, driven by circulation, lead to so-called first-order effects on thermodynamic profiles and turbulent surface fluxes, and (2) the advection is followed by transformation of the air through various physical processes, causing second-order effects. An example of second-order effects is the associated cloud formation, which shifts the strongest radiative cooling from the surface to the cloud top. The temperature and specific humidity profiles are most sensitive to large-scale circulation over the Eurasian land west of 90∘ E and the Arctic Ocean sea ice, whereas impacts over North America and Greenland are more ambiguous. Eurasian land, between 90 and 140∘ E, occasionally receives warm and moist air from the northern North Atlantic, which, with the support of radiative impacts of clouds, weakens the otherwise strong temperature and specific humidity inversions. Altitudes of maximum temperature and specific humidity in a profile and their variability between the circulation types are good indicators of the depth of the layer impacted by surface–atmosphere processes interacting with the large-scale circulation. Different circulation types typically cause variations of a few hundred metres to this altitude, and the layer impacted is deepest over north-eastern Eurasia and North America.

The Vertical City Weather Generator (VCWG v1.3.2)

Abstract. The Vertical City Weather Generator (VCWG) is a computationally efficient urban microclimate model developed to predict temporal and vertical variation of potential temperature, wind speed, specific humidity, and turbulent kinetic energy. It is composed of various sub-models: a rural model, an urban vertical diffusion model, a radiation model, and a building energy model. Forced with weather data from a nearby rural site, the rural model is used to solve for the vertical profiles of potential temperature, specific humidity, and friction velocity at 10 m a.g.l. The rural model also calculates a horizontal pressure gradient. The rural model outputs are applied to a vertical diffusion urban microclimate model that solves vertical transport equations for potential temperature, momentum, specific humidity, and turbulent kinetic energy. The urban vertical diffusion model is also coupled to the radiation and building energy models using two-way interaction. The aerodynamic and thermal effects of urban elements, surface vegetation, and trees are considered. The predictions of the VCWG model are compared to observations of the Basel UrBan Boundary Layer Experiment (BUBBLE) microclimate field campaign for 8 months from December 2001 to July 2002. The model evaluation indicates that the VCWG predicts vertical profiles of meteorological variables in reasonable agreement with the field measurements. The average bias, root mean square error (RMSE), and R2 for potential temperature are 0.25 K, 1.41 K, and 0.82, respectively. The average bias, RMSE, and R2 for wind speed are 0.67 m s−1, 1.06 m s−1, and 0.41, respectively. The average bias, RMSE, and R2 for specific humidity are 0.00057 kg kg−1, 0.0010 kg kg−1, and 0.85, respectively. In addition, the average bias, RMSE, and R2 for the urban heat island (UHI) are 0.36 K, 1.2 K, and 0.35, respectively. Based on the evaluation, the model performance is comparable to the performance of similar models. The performance of the model is further explored to investigate the effects of urban configurations such as plan and frontal area densities, varying levels of vegetation, building energy configuration, radiation configuration, seasonal variations, and different climate zones on the model predictions. The results obtained from the explorations are reasonably consistent with previous studies in the literature, justifying the reliability and computational efficiency of VCWG for operational urban development projects.

Estimating and Removing the Sampling Biases of the AIRS Obs4MIPs V2 Data.

The Atmospheric Infrared Sounder (AIRS) Observations for Model Intercomparison Projects (Obs4MIPs) Version 2.0 (V2.0) monthly mean tropospheric air temperature, specific humidity, and relative humidity profile data were designed for climate model evaluation in the context of the Coupled Model Intercomparison Project (CMIP). Due to the limitations of the Aqua satellite orbit and the AIRS retrieval algorithm, the sampling biases of the AIRS Obs4MIPs V2.0 data can be large for certain cases and must be considered when the AIRS Obs4MIPs V2.0 data are used for climate model evaluation. In this study, we estimate the sampling biases of the AIRS Obs4MIPs V2.0 data based on the fifth generation of the European Centre for Medium‐Range Weather Forecasts (ECMWF) (ERA5) reanalysis and cross‐check them using the Modern‐Era Retrospective Analysis for Research and Application, Version 2 (MERRA‐2) reanalysis. We then remove the estimated sampling biases from the AIRS Obs4MIPs V2.0 data and produce the sampling‐bias‐corrected AIRS Obs4MIPs V2.1 data that have been published at the Earth System Grid Federation (ESGF) data centers and should be used in the future for climate model evaluation.

Assessment of nonlinear effects of a deep subgrid lake with an atmospheric single‐column model

AbstractSome nonlinear aspects due to the coupling of a single column atmospheric model (SCM) with both a land‐surface model (LSM) and a lake model has lately been published by Goyette in 2017. In this previous study, the surface type abruptly changed from land to an open water surface. This article further investigates the potential of this SCM to explore the nonlinear effects through the analysis of the impacts of fractional land and open water surfaces on the surface fluxes and variables, as well as on the atmospheric air temperature, specific humidity, and wind speed vertical profiles, with an application to deep Lake Geneva, Switzerland. It is shown that the coupling methodology depending on the surface type produced realistic and coherent results and indicated that the mean fluxes and other relevant quantities may not be simply averaged on the basis of the fractional area of the land and that of the open water. If Lake Geneva relative surface area is prescribed to its observed value with respect to the model grid‐mesh size, the remaining being land surface, results showed maximum seasonal mean differences of 7.5 and 5.4 W·m−2 for the latent and sensible heat flux in Autumn respectively, 1.6°C, 0.8 g·kg−1 for the screen‐level air temperature and specific humidity in Autumn respectively, and 0.6 m·s−1 for the anemometer‐level windspeed in Winter with respect to the original land surface, owing to the different behaviour of the vertical turbulent transfer of heat, moisture and momentum but also to the different thermal and radiative characteristics of land and water surfaces.

Assessments of the Retrieval of Atmospheric Profiles from GNSS Radio Occultation Data in Moist Tropospheric Conditions Using Radiosonde Data

The Global Navigation Satellite System (GNSS) Radio Occultation (RO) retrieved temperature and specific humidity profiles can be widely used for weather and climate studies in troposphere. However, some aspects, such as the influences of background data on these retrieved moist profiles have not been discussed yet. This research evaluates RO retrieved temperature and specific humidity profiles from Wegener Center for Climate and Global Change (WEGC), Radio Occultation Meteorology Satellite Application Facility (ROM SAF) and University Corporation for Atmospheric Research (UCAR) Boulder RO processing centers by comparing with measurements from 10 selected Integrated Global Radiosonde Archive (IGRA) radiosonde stations in different latitudinal bands over 2007 to 2010. The background profiles used for producing their moist profiles are also compared with radiosonde. We found that RO retrieved temperature profiles from all centers agree well with radiosonde. Mean differences at polar, mid-latitudinal and tropical stations are varying within ±0.2 K, ±0.5 K and from −1 to 0.2 K, respectively, with standard deviations varying from 1 to 2 K for most pressure levels. The differences between RO retrieved and their background temperature profiles for WEGC are varying within ±0.5 K at altitudes above 300 hPa, and the differences for ROM SAF are within ±0.2 K, and that for UCAR are within 0.5 K at altitudes below 300 hPa. Both RO retrieved and background specific humidity above 600 hPa are found to have large positive differences (up to 40%) against most radiosonde measurements. Discrepancies of moist profiles among the three centers are overall minor at altitudes above 300 hPa for temperature and at altitudes above 700 hPa for specific humidity. Specific humidity standard deviations are largest at tropical stations in June July August months. It is expected that the outcome of this research can help readers to understand the characteristics of moist products among centers.

Differences in tropical high clouds among reanalyses: origins and radiative impacts

Abstract. We examine differences among reanalysis high-cloud products in the tropics, assess the impacts of these differences on radiation budgets at the top of the atmosphere and within the tropical upper troposphere and lower stratosphere (UTLS), and discuss their possible origins in the context of the reanalysis models. We focus on the ERA5 (fifth-generation European Centre for Medium-range Weather Forecasts – ECMWF – reanalysis), ERA-Interim (ECMWF Interim Reanalysis), JRA-55 (Japanese 55-year Reanalysis), MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, Version 2), and CFSR/CFSv2 (Climate Forecast System Reanalysis/Climate Forecast System Version 2) reanalyses. As a general rule, JRA-55 produces the smallest tropical high-cloud fractions and cloud water contents among the reanalyses, while MERRA-2 produces the largest. Accordingly, long-wave cloud radiative effects are relatively weak in JRA-55 and relatively strong in MERRA-2. Only MERRA-2 and ERA5 among the reanalyses produce tropical-mean values of outgoing long-wave radiation (OLR) close to those observed, but ERA5 tends to underestimate cloud effects, while MERRA-2 tends to overestimate variability. ERA5 also produces distributions of long-wave, short-wave, and total cloud radiative effects at the top of the atmosphere that are very consistent with those observed. The other reanalyses all exhibit substantial biases in at least one of these metrics, although compensation between the long-wave and short-wave effects helps to constrain biases in the total cloud radiative effect for most reanalyses. The vertical distribution of cloud water content emerges as a key difference between ERA-Interim and other reanalyses. Whereas ERA-Interim shows a monotonic decrease of cloud water content with increasing height, the other reanalyses all produce distinct anvil layers. The latter is in better agreement with observations and yields very different profiles of radiative heating in the UTLS. For example, whereas the altitude of the level of zero net radiative heating tends to be lower in convective regions than in the rest of the tropics in ERA-Interim, the opposite is true for the other four reanalyses. Differences in cloud water content also help to explain systematic differences in radiative heating in the tropical lower stratosphere among the reanalyses. We discuss several ways in which aspects of the cloud and convection schemes impact the tropical environment. Discrepancies in the vertical profiles of temperature and specific humidity in convective regions are particularly noteworthy, as these variables are directly constrained by data assimilation, are widely used, and feed back to convective behaviour through their relationships with thermodynamic stability.

Analytical study of the performance of the IMERG over the Indian landmass

AbstractThe paper compares the final run of Integrated Multi‐Satellite Retrieval of Global Precipitation Mission (IMERG) products with India Meteorological Department (IMD) gridded data over the Indian land mass during the southwest monsoon period (June–September) in the period 2014–2017. Spatiotemporal variations of the IMERG are evaluated with IMD rainfall by employing different statistical techniques, and the capability of the IMERG is examined using the categorical skill metrics. A region in central India is the focus in order to study the southwest monsoon's static and dynamic characteristics, such as rainfall distribution and monsoon activity, using the IMERG in conjunction with IMD rainfall data sets. The integrated condensation rate (ICR) was estimated using the specific humidity profiles, and it correlated with the IMERG and IMD rainfall. The IMERG is found to be a potential source for adequately reflecting the ground gauge‐gridded data of categorical rainfall amounts, from very light rain (trace–2.4 mm) to very heavy rain (about 115.6–204.4 mm). However, the IMERG does not reflect satisfactorily the extreme heavy rain events (≥ 204.5 mm·day–1) during the study period. The significant correlation between IMERG/IMD rainfall and the ICR suggests that improved adjustments methods are required for better results when depicting accurate extreme heavy rainfall events by the IMERG.

Multi-nested WRF simulations for studying planetary boundary layer processes on the turbulence-permitting scale in a realistic mesoscale environment

The Weather Research and Forecasting (WRF) model was applied in a nested configuration from a 2.7 km convection-permitting domain via grey-zone resolutions of 900 m and 300 m down to the 100 m turbulence-permitting scale. Based on sensitivity studies, this approach was optimized to investigate the evolution of small-scale processes in the PBL for a clear sky case during the HOPE experiment in western Germany on 24 April 2013. The results were compared with theoretical and experimental findings from literature and high-resolution lidar observations collected during the campaign. Simulations with parameterized turbulence were able to capture the temporal evolution of the PBL height, but almost no internal structure was simulated in the boundary layer. Only the turbulence-permitting simulations were capable of reproducing the morning transition from the stable nighttime to the daytime convective boundary layer and the following break-up into turbulent eddies. Comparisons with lidar data showed that the turbulence-permitting simulations reproduced the observed turbulence statistics. Nevertheless, the potential temperature in the boundary layer was 1 K cooler than observed, caused by a lower surface temperature mixed upward by the turbulent eddies. The simulated PBL height was underestimated by 200 m, reflected in a well-captured profile of specific humidity up to a height of 900 m and an overly strong decrease of moisture above. The general shape of the variance profiles of potential temperature and specific humidity were captured by the model. However, the simulated variability throughout the boundary layer was lower and the different heights of the variance peaks indicated that the model may not fully capture the turbulent processes at the top of the boundary layer. Identifying those systematic differences between nested simulations and observations demonstrated the value of this model approach for process studies and parameterization tests.

Improving heavy rainfall forecasts by assimilating surface precipitation in the convective scale model AROME: A case study of the Mediterranean event of November 4, 2017

AbstractThe ability of precipitation assimilation is assessed in a convective scale model in order to improve the precipitation forecast for a Mediterranean heavy rain event that took place on November 4, 2017. The proposed assimilation method is based on a two‐step approach. First, one‐dimensional variational (1D‐Var) assimilation is applied on hourly accumulated precipitation to retrieve temperature and specific humidity profiles. These retrieved profiles are then combined in relative humidity profiles before being assimilated into the AROME (Application of Research to Operational at MEsoscale) 3D‐Var system. Three experiments are run for this case study. The results show that precipitation assimilation has a positive impact on both moisture analysis and the forecast of dynamic fields. A comparison of 24 hr‐accumulated precipitation forecasts with precipitation analysis from radar and gauge data (ANTILOPE) demonstrates the ability of rain assimilation to improve convective precipitation forecasts. A statistical evaluation against rain gauges indicates better scores due to the additional moisture information given by the precipitation assimilation.