Spatio-temporal variability of near-surface air pollutants at four distinct geographical locations in Andhra Pradesh State of India

Measuring submesoscale variability is the core task of the field campaign FESSTVaL (Field Experiment on Sub-Mesoscale Spatio-Temporal Variability in Lindenberg).&#160; FESSTVaL focuses on three sources of submesoscale variability: cold pools, wind gusts and boundary layer pattern. It took place in the summer months of 2021 at the Meteorological Observatory Lindenberg &#8211; Richard-A&#223;mann-Observatory (MOL-RAO) of the German Weather Service (DWD) near Berlin and was initiated by the Hans-Ertel-Center for Weather Research (HErZ).In order to capture phenomena at the submesoscale (500 m &#8211; 5 km), generally not captured by conventional measurement network, a hierarchical measurement strategy is adopted. This includes wind profiling stations with a coordinated scanning strategy of several Doppler Lidars, two mobile profilers to measure thermodynamic properties and precipitation, more than 100 stations with near-surface measurements of air temperature, pressure and soil moisture, more than 20 automatic weather stations, an X-Band radar, and a number of energy balance stations. This equipment is augmented by the extensive ground-based remote sensing array at the MOL-RAO, operated by DWD and by flights operated by Unmanned Aerial Systems. Complementing to this, the benefit of a citizen-science measurement network is investigated during the campaign with &#8220;Internet-of-things&#8221; based technology and low-cost sensors built and maintained by citizens. The measurements are supplemented by high-resolution large-eddy simulations (ICON-LES).Originally planned for the summer 2020, FESSTVaL had to be postponed to 2021 and replaced by three local individual campaigns, conducted in Bayern, Lindenberg and Hamburg in 2020. Those three test campaigns demonstrated the ability of the envisionned measurement strategy and planned instruments to capture submesoscale variability and submesoscale weather phenomean. This talk will give a brief overview on the results of these three campaigns, as a foretaste to FESSTVaL, together with some of the very first measurements taken during FESSTVaL.

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Spatio-temporal variability of near-surface air temperature in the Dokriani glacier catchment (DGC), central Himalaya

Theoretical and Applied Climatology ◽

10.1007/s00704-018-2544-z ◽

2018 ◽

Vol 136 (3-4) ◽

pp. 1513-1532 ◽

Cited By ~ 5

Author(s):

Jairam Singh Yadav ◽

Bhanu Pratap ◽

Anil K. Gupta ◽

D. P. Dobhal ◽

R. B. S. Yadav ◽

...

Keyword(s):

Air Temperature ◽

Temporal Variability ◽

Surface Air Temperature ◽

Central Himalaya ◽

Near Surface ◽

Spatio Temporal

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Analyzing Spatio-Temporal Factors to Estimate the Response Time between SMOS and In-Situ Soil Moisture at Different Depths

Remote Sensing ◽

10.3390/rs12162614 ◽

2020 ◽

Vol 12 (16) ◽

pp. 2614

Author(s):

Christoph Herbert ◽

Miriam Pablos ◽

Mercè Vall-llossera ◽

Adriano Camps ◽

José Martínez-Fernández

Keyword(s):

Time Series ◽

Soil Moisture ◽

Response Time ◽

Temporal Variability ◽

Characteristic Time ◽

Time Lag ◽

Near Surface ◽

Time Length ◽

Spatio Temporal

A comprehensive understanding of temporal variability of subsurface soil moisture (SM) is paramount in hydrological and agricultural applications such as rainfed farming and irrigation. Since the SMOS (Soil Moisture and Ocean Salinity) mission was launched in 2009, globally available satellite SM retrievals have been used to investigate SM dynamics, based on the fact that useful information about subsurface SM is contained in their time series. SM along the depth profile is influenced by atmospheric forcing and local SM properties. Until now, subsurface SM was estimated by weighting preceding information of remotely sensed surface SM time series according to an optimized depth-specific characteristic time length. However, especially in regions with extreme SM conditions, the response time is supposed to be seasonally variable and depends on related processes occurring at different timescales. Aim of this study was to quantify the response time by means of the time lag between the trend series of satellite and in-situ SM observations using a Dynamic Time Warping (DTW) technique. DTW was applied to the SMOS satellite SM L4 product at 1 km resolution developed by the Barcelona Expert Center (BEC), and in-situ near-surface and root-zone SM of four representative stations at multiple depths, located in the Soil Moisture Measurements Station Network of the University of Salamanca (REMEDHUS) in Western Spain. DTW was customized to control the rate of accumulation and reduction of time lag during wetting and drying conditions and to consider the onset dates of pronounced precipitation events to increase sensitivity to prominent features of the input series. The temporal variability of climate factors in combination with crop growing seasons were used to indicate prevailing SM-related processes. Hereby, a comparison of long-term precipitation recordings and estimations of potential evapotranspiration (PET) allowed us to estimate SM seasons. The spatial heterogeneity of land use was analyzed by means of high-resolution images of Normalized Difference Vegetation Index (NDVI) from Sentinel-2 to provide information about the level of spatial representativeness of SMOS observations to each in-situ station. Results of the spatio-temporal analysis of the study were then evaluated to understand seasonally and spatially changing patterns in time lag. The time lag evolution describes a variable characteristic time length by considering the relevant processes which link SMOS and in-situ SM observation, which is an important step to accurately infer subsurface SM from satellite time series. At a further stage, the approach needs to be applied to different SM networks to understand the seasonal, climate- and site-specific characteristic behaviour of time lag and to decide, whether general conclusions can be drawn.

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How effective is information on soil-landscape units for determining spatio-temporal variability of near-surface soil moisture?

Journal of Agricultural Engineering ◽

10.4081/jae.2018.822 ◽

2018 ◽

Vol 49 (3) ◽

pp. 174-182 ◽

Cited By ~ 3

Author(s):

Paolo Nasta ◽

Benedetto Sica ◽

Caterina Mazzitelli ◽

Paola Di Fiore ◽

Ugo Lazzaro ◽

...

Keyword(s):

Soil Moisture ◽

Temporal Variability ◽

Surface Soil ◽

Controlling Factors ◽

Surface Soil Moisture ◽

Near Surface ◽

Soil Landscape ◽

Spatio Temporal ◽

Landscape Units ◽

Mapping Units

In the last decades, a growing interest in fostering advanced interdisciplinary studies is leading to the establishment of observatories in pilot catchments for long-term monitoring of hydrological variables and fluxes. Nevertheless prior to sensor network installation, this investment necessitates preliminary surveys on key-variables such as near-surface soil moisture in order to prevent risks of erronously distributing sensors by missing sufficient spatial information for understanding hydrological processes within the landatmosphere interactions. The availability of maps describing areas with similar morphological, topographical, soil, and vegetation characteristics enable preliminary surveys to be organized for capturing spatio-temporal variability of soil moisture as best as possible. The soil-landscape classification can be considered as an interesting approach for grouping mapping units with similar hydrological behavior. Therefore, we assume the soil-landscape units as hydrotopes or hydrological similar units. Six transects were established along two hillsides of the Upper Alento River catchment (southern Italy) which is a proper candidate to become a Critical Zone Observatory. In this paper we use a soil-landscape map to infer spatial and temporal dynamics of soil moisture measured along these transects, whereas quantitative analyses were obtained by using multivariate techniques. The effectiveness of available information on soil-landscape mapping units is evaluated with respect to different observed patterns of soil moisture: wetter- and drier-than average observation points belong to agricultural and forested hillslopes, respectively. Soil texture and topographical controlling factors, especially clay content and slope gradient, are found to explain approximately 70% of the observed spatial variations in soil moisture along the forested hillslopes. The spatial structure explained by the environmental controlling factors decreases to 45% in the cases of the agricultural hillslopes mainly due to perturbations induced by grazing and tillage practices.

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Variational regional inverse modeling of reactive species emissions with PYVAR-CHIMERE

10.5194/gmd-2019-186 ◽

2019 ◽

Cited By ~ 2

Author(s):

Audrey Fortems-Cheiney ◽

Isabelle Pison ◽

Gaelle Dufour ◽

Grégoire Broquet ◽

Antoine Berchet ◽

...

Keyword(s):

Particulate Matter ◽

Inverse Modeling ◽

Temporal Variability ◽

Tropospheric Ozone ◽

Air Pollutants ◽

Reactive Species ◽

Satellite Observations ◽

Emission Inventories ◽

Key Drivers ◽

Spatio Temporal

Abstract. Up-to-date and accurate emission inventories for air pollutants are essential for understanding their role in the formation of tropospheric ozone and particulate matter at various temporal scales, for anticipating pollution peaks and for identifying the key drivers that could help mitigate their emissions. This paper describes the Bayesian variational inverse system PYVAR-CHIMERE, which is adapted to the inversion of reactive species. Complementarily with bottom-up inventories, this system aims at updating and improving the knowledge on the high spatio-temporal variability of emissions of air pollutants and their precursors. The system is designed to use any type of observations, such as satellite observations or surface stations. The potential of PYVAR-CHIMERE is illustrated with one-day inversions of CO and NO2 emissions in Europe, using the MOPITT and OMI satellite observations (for CO and for NO2, respectively).

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Distributed summer air temperatures across mountain glaciers: climatic sensitivity and glacier size

10.5194/tc-2020-198 ◽

2020 ◽

Author(s):

Thomas E. Shaw ◽

Wei Yang ◽

Álvaro Ayala ◽

Claudio Bravo ◽

Chaunxi Zhao ◽

...

Keyword(s):

Tibetan Plateau ◽

Temporal Variability ◽

Surface Air Temperature ◽

Near Surface ◽

Mountain Glaciers ◽

Air Temperatures ◽

Climatic Sensitivity ◽

The World ◽

Spatio Temporal ◽

Glacier Ablation

Abstract. Near-surface air temperature (Ta) is highly important for modelling glacier ablation, though its spatio-temporal variability over melting glaciers still remains largely unknown. We present a new dataset of distributed Ta for three glaciers of different size in the south-east Tibetan Plateau during two monsoon-dominated summer seasons. We compare on-glacier Ta to ambient Ta extrapolated from several, local off-glacier stations. We parameterise the along-flowline climatic sensitivity of Ta on these glaciers to changes in off-glacier temperatures and present the results in the context of several available distributed on-glacier datasets around the world. Climatic sensitivity decreases rapidly up to 2000–3000 m along the down-glacier flowline distance. Beyond this distance, both the Ta of the Tibetan glaciers and global glacier datasets show a slower decrease of climatic sensitivity. In general, observations on small glaciers (with

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