Wind waves in the North Atlantic from ship navigational radar: SeaVision development and its validation with Spotter wave buoy and WaveWatch III

The global coverage of the observational network of the wind waves is still characterized by the significant gaps in in situ observations. At the same time wind waves play an important role into the Earth’ climate system specifically in the air-sea interaction processes and energy exchange between the ocean and the atmosphere. In this paper we present the SeaVision system for measuring wind waves’ parameters in the open ocean with navigational marine X-band radar and prime data collection from the three research cruises in the North Atlantic (2020 and 2021) and Arctic (2021). Simultaneously with SeaVision observations of the wind waves we were collecting data in the same locations and time with Spotter wave buoy and running WaveWatch III model over our domains. Measurements with SeaVision were quality controlled and validated by comparison with Spotter buoy data and WaveWatch III experiments. Observations of the wind waves with navigational Xband radar are in agreement among these three sources of data, with the best agreement for wave propagation directions. The dataset that supports this paper consists of significant wave height, wave period and wave energy frequency spectrum from both SeaVision and Spotter buoy. Currently the dataset is available through the temporary link (https://sail.ocean.ru/tilinina2021/) while supporting dataset (Tilinina et al., 2021) is in technical processing at PANGAEA repository. The dataset can be used for validation of satellite missions as well as model outputs. One of the major highlights in this study is potential of all ships navigating into the open ocean and equipped with X-band marine radar to participate into the development of another observational network for the wind waves in the open ocean once cheap and independently operating version of the SeaVision (or any other system) is available.

In-situ observational network for extreme weather events in India

Extreme weather events, interacting with vulnerable human and natural systems, can lead to disasters, especially in absence of responsive social system. Accurate and timely monitoring and forecast of heavy rains, tropical cyclones, thunderstorms, hailstorms, cloudburst, drought, heat and cold waves, etc. are required to respond effectively to such events. Due to extreme weather events, crops over large parts of the country are adversely affected reducing production of total food grains, fodder, cash crops, vegetables and fruits which in turn affect the earnings and livelihood of individual farmers as well as the economy of the country. In situ observational network are the vital component for skilful prediction of extreme weather events. Current observational requirements for extreme weather prediction are met, to varying degrees by a range of in-situ observing systems and space-based systems. The augmentation of in-situ observational network is continuously progressing. IMD now has a network of Doppler Weather Radars (DWRs), Automatic Weather Stations (AWSs), Agro AWSs, Automatic Rain Gauges (ARGs), GPS upper air systems etc. These observations along with non-conventional (satellite) data are now being used to run its global and regional numerical prediction models on High Performance Computing Systems (HPCS). This has improved monitoring and forecasting capabilities for extreme weather events like cyclones, severe thunderstorm, heavy rainfall and floods in a significant manner. This paper provides an overview of the role of in-situ observational network for extreme weather events in India, framework for further augmentation to the network and other requirements to further enhance capabilities for high impact & extreme weather events and natural hazards.

Aerosol properties and aerosol–radiation interactions in clear sky conditions over Germany

The clear-sky radiative effect of aerosol-radiation interactions is of relevance for our understanding of the climate system. The influence of aerosol on the surface energy budget is of high interest for the renewable energy sector. In this study, the radiative effect is investigated in particular with respect to seasonal and regional variations for the region of Germany and the year 2015 at the surface and top of atmosphere using two complementary approaches. First, an ensemble of clear-sky models which explicitly consider aerosols is utilized to retrieve the aerosol optical depth and the surface direct radiative effect of aerosols by means of a clear sky fitting technique. For this, short-wave broadband irradiance measurements in the absence of clouds are used as a basis. A clear sky detection algorithm is used to identify cloud free observations. Considered are measurements of the shortwave broadband global and diffuse horizontal irradiance with shaded and unshaded pyranometers at 25 stations across Germany within the observational network of the German Weather Service (DWD). Clear sky models used are MMAC, MRMv6.1, METSTAT, ESRA, Heliosat-1, CEM and the simplified Solis model. The definition of aerosol and atmospheric characteristics of the models are examined in detail for their suitability for this approach. Second, the radiative effect is estimated using explicit radiative transfer simulations with inputs on the meteorological state of the atmosphere, trace-gases and aerosol from CAMS reanalysis. The aerosol optical properties (aerosol optical depth, Ångström exponent, single scattering albedo and assymetrie parameter) are first evaluated with AERONET direct sun and inversion products. The largest inconsistency is found for the aerosol absorption, which is overestimated by about 0.03 or about 30 % by the CAMS reanalysis. Compared to the DWD observational network, the simulated global, direct and diffuse irradiances show reasonable agreement within the measurement uncertainty. The radiative kernel method is used to estimate the resulting uncertainty and bias of the simulated direct radiative effect. The uncertainty is estimated to −1.5 ± 7.7 and 0.6 ± 3.5 W m−2 at the surface and top of atmosphere, respectively, while the annual-mean biases at the surface, top of atmosphere and total atmosphere are −10.6, −6.5 and 4.1 W m−2, respectively. The retrieval of the aerosol radiative effect with the clear sky models shows a high level of agreement with the radiative transfer simulations, with an RMSE of 5.8 W m−2 and a correlation of 0.75. The annual mean of the REari at the surface for the 25 DWD stations shows a value of −12.8 ± 5 W m−2 as average over the clear sky models, compared to −11 W m−2 from the radiative transfer simulations. Since all models assume a fixed aerosol characterisation, the annual cycle of the aerosol radiation effect cannot be reproduced. Out of this set of clear sky models, the largest level of agreement is shown by the ESRA and MRMv6.1 models.

Analysis of the arrival of southern cyclones to the Republic of Bashkortostan

Analysis of the arrival of southern cyclones to the Republic of Bashkortostan / Volobueva O.V., Toptunova O.N., Drobzheva Y.V. // Hydrometeorological Research and Forecasting, 2021, no. 2 (380), pp. 66-76. The results of the analysis of the arrival of southern cyclones to the territory of the Republic of Bashkortostan for the period of 1993-2018 are presented. Typical trajectories of cyclones, areas of their origin, as well as accompanying adverse weather phenomena are identified according to the observational network of the Bashkir Administration for Hydrometeorology and Environmental Monitoring. In 70 % of cases, southern cyclones move through the territory of the republic and leave it in the southern direction. About 50 % of the depressions come to the Republic of Bashkortostan from the Black Sea and its coast. The upper-air field for the arrival of southern cyclones is the front part of the pressure trough with high geopotential height gradients. The arrivals of southern cyclones have a significant impact on the study area and are closely related to the formation of severe hydrometeorological events. Keywords: southern cyclone, typical territories, the Republic of Bashkortost

Homogenisation of monthly temperatures of the Swedish observational network 1850-2020

<p>Monthly averages of statistical temperature variables (i.e. monthly averages of daily maximum, minimum, and mean temperatures) are homogenised for a large part of the Swedish observational network dataset from 1850 to 2020. Data from 573–587 weather stations (depending on variable) are coupled into 299–303 time series. The coupling of time series is partly performed automatically following a set of criteria of geographical proximity, altitude, proximity to coast line, time series overlap, and correlation of the data series.</p><p>The homogenisation of the data set is performed with the recently developed homogenisation tool Bart. Bart is a fully automatic modification of the homogenisation tool HOMER. Bart uses a set of input parameters to accept or reject potential homogeneity break points suggested by the different functions of HOMER. Bart performs correction and gap filling of the data series according to the accepted homogeneity break points. A rudimentary sensitivity test is performed to examine how sensitive the homogenisation is to the selection of the input parameters assumed most important and to find a optimal set up of these parameters. Other features in Bart include a novel procedure for the selection of reference time series to account for uneven data coverage, and parallel computing to reduce the computational time.</p><p>An important application of the homogenised data set is the calculation of the climate indicator of temperature. The climate indicator of temperature is the average annual mean temperatures of thirty-nine weather stations, carefully selected to represent the climate in Sweden over the last 170 years. The use of homogenised data gives a 1.8 °C (10 a)-1 greater warming than if raw data is used from 1860 to present, the period for which data coverage is sufficient.</p><p><img src="https://contentmanager.copernicus.org/fileStorageProxy.php?f=gnp.18ad2b13c10069128270161/sdaolpUECMynit/12UGE&app=m&a=0&c=73e0a62bb7be6a58ba81126e3ce2b48e&ct=x&pn=gnp.elif&d=1" alt=""></p>

GuMNet – The Guadarrama Monitoring Network initiative (Spain)

<p>GuMNet is a facility that operates continuous observation of the atmosphere, surface and subsurface at the Sierra de Guadarrama, located 50 km north-northwest of Madrid. It is composed of 10 real–time automatic stations and attempts to promote research on weather, soil thermodynamics, boundary layer physics, impacts of climate change on climate and ecosystems and air pollution in Sierra de Guadarrama. This infrastructure represents a first step into providing a unique observational network in a high protected environment that can serve a wide range of scientific and educational interests and also management.</p><p>The stations are located at heights ranging from 900 m.a.s.l. to 2225 m.a.s.l. Every station has been settled in open areas, except for one that can be found in a forested zone. High altitude sites are focused on periglacial areas, while low elevation sites are placed in pasture environments. The atmospheric instrumentation includes sensors used for the measurement of air temperature, air humidity, 4-component radiation, solid and liquid precipitation, snow depth, wind speed and wind direction. For the subsurface measurements, soil temperature and humidity sensors have been placed in 9 trenches up to 1 m depth and 12 boreholes up to 2 m and 20 m depth. One of the lowest stations has been equipped with a 3D sonic anemometer that includes a CO2/H2O analyzer. Wind profiles and eddy-covariance will be sampled, which is important for energy and water vapor exchanges. A portable station has also been equipped with a 3D sonic anemometer, which will enable the comparison between measurements at both sites. The entire network is connected via general packet radio service (GPRS) to the management software at the central laboratory located at the Campus of Excellence of Moncloa (Madrid, Spain).</p><p>The database generated by GuMNet is accessible through request and allows for developing studies concerning environmental and climate change in middle and high mountain areas. This valuable source of data aims at generating a space for scientific collaboration with other national and international institutions. The diversity of potential uses of the GuMNet observational network will be very useful in education at every level.</p><p>Website and contact: http://www.ucm.es/gumnet/</p>

Unravelling the process controls of the spatial coherence of precipitation

<p>Precipitation is a key input variable for precipitation-runoff models. For catchments without precipitation observations generating rainfall fields is a possibility to enable precipitation-runoff simulations. These synthetic precipitation fields have to reproduce the spatial precipitation distribution adequately, especially at large catchment scales. Since the spatial precipitation coherence in ungauged catchments is unknown, it has to be transferred from an existing observational network. Ideally, the meteorological regime of the area of the observational network should be similar to that of the ungauged catchment in terms of the processes and factors controlling the spatial precipitation coherence.</p><p>This study identifies these processes and conceptualises them for rainfall modelling. We analyse precipitation time series of 1200 stations in the Greater Alpine Region (including Austria and Southern Germany, ~300,000 km²). Precipitation data subsets are constructed based on space-dependent (including climate zone, land use, altitude, slope, exposition) and time-dependent factors (seasons, circulation patterns, temperature). The analyses are carried out for different temporal resolutions (1, 12 and 24 hours) to unravel possible time-dependencies. The spatial precipitation coherence is represented by bivariate characteristics (Pearson’s correlation coefficient, continuity ratio, probability of occurrence) as a function of station separation distance. Uncertainty and variability of the spatial coherence are quantified via function spaces. Self-organizing maps are applied to translate the multi- dimensional results into low-dimensional maps.</p><p>In the low lands of the study domain, time-dependent factors are expected to influence the spatial precipitation coherence stronger than space-dependent factors, while in the mountainous regions the space-dependent factors will have a stronger influence due to the air movement being forced by the topography.</p>