In situ
                    observations of the influence of a large onshore wind farm on near-surface temperature, turbulence intensity and wind speed profiles

The Ocean-surface Heterogeneity MApping (OHMA) algorithm is an objective, replicable approach to map the spatio-temporal heterogeneity of ocean surface waters. It is used to processes hypertemporal, satellite-derived data and produces a single-image surface heterogeneity (SH) dataset for the selected parameter of interest. The product separates regions of dissimilar temporal characteristics. Data validation is challenging because it requires In-situ observations at spatial and temporal resolutions comparable to the hyper-temporal inputs. Validating this spatio-temporal product highlighted the need to optimise existing vessel-based data collection efforts, to maximise exploitation of the rapidly-growing hyper-temporal data resource.For this study, the SH was created using hyper-temporal 1km resolution satellite derived Sea Surface Temperature (SST) data acquired in 2011. Underway ship observations of near surface temperature collected on multiple scientific surveys off the Irish coast in 2011 were used to validate the results. The most suitable underway ship SST data were identified in ocean areas sampled multiple times and with representative measurements across all seasons.A 3-stage bias reduction approach was applied to identify suitable ocean areas. The first bias reduction addressed temporal bias, i.e., the temporal spread of available In-situ ship transect data across each satellite image pixel. Only pixels for which In-situ data were obtained at least once in each season were selected; resulting in 14 SH image pixels deemed suitable out of a total of 3,677 SH image pixels with available In-situ data. The second bias reduction addressed spatial bias, to reduce the influence of over-sampled areas in an image pixel with a sub-pixel approach. Statistical dispersion measures and statistical shape measures were calculated for each of the sets of sub-pixel values. This gave heterogeneity estimates for each cruise transit of a pixel area. The third bias reduction addressed bias of temporally oversampled seasons. For each transit-derived heterogeneity measure, the values within each season were averaged, before the annual average value was derived across all four seasons in 2011.Significant associations were identified between satellite SST-derived SH values, and In-situ heterogeneity measures related to shape; absolute skewness (Spearman&#8217;s Rank, n=14, &#961;[12]= +0.5755, P<0.05), and kurtosis (Spearman&#8217;s Rank, n=14, &#961;[12] = 0.5446, P < 0.05). These are a consequence of (i) locally-extreme measurements, and/or (ii) increased presence of sharp transitions detected spatially by In-situ data. However, the number and location of suitable In-situ validation sites precluded a robust validation of the SH dataset (14 pixels located in Irish waters, for a dataset spanning the North Atlantic). This requires more targeted data. The approach would have benefited from more samples over the winter season, which would have enabled more offshore validation sites to be incorporated into the analysis. This is a challenge that faces satellite product developers, who want to deliver spatio-temporal information to new markets. There is a significant opportunity for dedicated, transit-measured (e.g. Ferry box data), validation sites to be established. These could potentially synergise with key nodes in global shipping routes to maximise data collected by vessels of opportunity, and ensure consistent data are collected over the same area at regular intervals.

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Evaluation of regional NWP results for sea ice covered Bothnia Bay (Baltic Sea) in winter 2020.

10.5194/egusphere-egu21-14612 ◽

2021 ◽

Author(s):

Marta Wenta ◽

Agnieszka Herman

Keyword(s):

Boundary Layer ◽

Wind Speed ◽

Sea Ice ◽

Surface Temperature ◽

Atmospheric Boundary Layer ◽

Baltic Sea ◽

Polar Regions ◽

Near Surface ◽

Accurate Representation ◽

Complex Processes

The ongoing development of NWP (Numerical Weather Prediction) models and their increasing horizontal resolution have significantly improved forecasting capabilities. However, in the polar regions models struggle with the representation of near-surface atmospheric properties and the vertical structure of the atmospheric boundary layer (ABL) over sea ice. Particularly difficult to resolve are near-surface temperature, wind speed, and humidity, along with diurnal changes of those properties. Many of the complex processes happening at the interface of sea ice and atmosphere, i.e. vertical fluxes, turbulence, atmosphere - surface coupling are poorly parameterized or not represented in the models at all. Limited data coverage and our poor understanding of the complex processes taking place in the polar ABL limit the development of suitable parametrizations. We try to contribute to the ongoing effort to improve the forecast skill in polar regions through the analysis of unmanned aerial vehicles (UAVs) and automatic weather station (AWS) atmospheric measurements from the coastal area of Bothnia Bay (Wenta et. al., 2021), and the application of those datasets for the analysis of regional NWP models' forecasts.&#160;Data collected during HAOS (Hailuoto Atmospheric Observations over Sea ice) campaign (Wenta et. al., 2021) is used for the evaluation of regional NWP models results from AROME (Applications of Research to Operations at Mesoscale) - Arctic, HIRLAM (High Resolution Limited Area Model) and WRF (Weather Research and Forecasting). The presented analysis focuses on 27 Feb. 2020 - 2 Mar. 2020, the time of the HAOS campaign, shortly after the formation of new, thin sea ice off the westernmost point of Hailuoto island.&#160; Throughout the studied period weather conditions changed from very cold (-14&#8451;), dry and cloud-free to warmer (~ -5&#8451;), more humid and opaquely cloudy. We evaluate models&#8217; ability to correctly resolve near-surface temperature, humidity, and wind speed, along with vertical changes of temperature and humidity over the sea ice. It is found that generally, models struggle with an accurate representation of surface-based temperature inversions, vertical variations of humidity, and temporal wind speed changes. Furthermore, a WRF Single Columng Model (SCM) is launched to study whether specific WRF planetary boundary layer parameterizations (MYJ, YSU, MYNN, QNSE), vertical resolution, and more accurate representation of surface conditions increase the WRF model&#8217;s ability to resolve the ABL above sea ice in the Bay of Bothnia. Experiments with WRF SCM are also used to determine the possible reasons behind model&#8217;s biases. Preliminary results show that accurate representation of sea ice conditions, including thickness, surface temperature, albedo, and snow coverage is crucial for increasing the quality of NWP models forecasts. We emphasize the importance of further development of parametrizations focusing on the processes at the sea ice-atmosphere interface.&#160;Reference:Wenta, M., Brus, D., Doulgeris, K., Vakkari, V., and Herman, A.: Winter atmospheric boundary layer observations over sea ice in the coastal zone of the Bay of Bothnia (Baltic Sea), Earth Syst. Sci. Data, 13, 33&#8211;42, https://doi.org/10.5194/essd-13-33-2021, 2021.&#160;

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The impact of near-surface soil moisture assimilation at subseasonal, seasonal, and inter-annual timescales

Hydrology and Earth System Sciences ◽

10.5194/hess-19-4831-2015 ◽

2015 ◽

Vol 19 (12) ◽

pp. 4831-4844 ◽

Cited By ~ 19

Author(s):

C. Draper ◽

R. Reichle

Keyword(s):

Soil Moisture ◽

Data Assimilation ◽

Surface Soil ◽

Root Zone ◽

Surface Model ◽

Surface Soil Moisture ◽

Near Surface ◽

In Situ Observations ◽

The Impact

Abstract. A 9 year record of Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) soil moisture retrievals are assimilated into the Catchment land surface model at four locations in the US. The assimilation is evaluated using the unbiased mean square error (ubMSE) relative to watershed-scale in situ observations, with the ubMSE separated into contributions from the subseasonal (SMshort), mean seasonal (SMseas), and inter-annual (SMlong) soil moisture dynamics. For near-surface soil moisture, the average ubMSE for Catchment without assimilation was (1.8 × 10−3 m3 m−3)2, of which 19 % was in SMlong, 26 % in SMseas, and 55 % in SMshort. The AMSR-E assimilation significantly reduced the total ubMSE at every site, with an average reduction of 33 %. Of this ubMSE reduction, 37 % occurred in SMlong, 24 % in SMseas, and 38 % in SMshort. For root-zone soil moisture, in situ observations were available at one site only, and the near-surface and root-zone results were very similar at this site. These results suggest that, in addition to the well-reported improvements in SMshort, assimilating a sufficiently long soil moisture data record can also improve the model representation of important long-term events, such as droughts. The improved agreement between the modeled and in situ SMseas is harder to interpret, given that mean seasonal cycle errors are systematic, and systematic errors are not typically targeted by (bias-blind) data assimilation. Finally, the use of 1-year subsets of the AMSR-E and Catchment soil moisture for estimating the observation-bias correction (rescaling) parameters is investigated. It is concluded that when only 1 year of data are available, the associated uncertainty in the rescaling parameters should not greatly reduce the average benefit gained from data assimilation, although locally and in extreme years there is a risk of increased errors.

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Local and Mesoscale Impacts of Wind Farms as Parameterized in a Mesoscale NWP Model

Monthly Weather Review ◽

10.1175/mwr-d-11-00352.1 ◽

2012 ◽

Vol 140 (9) ◽

pp. 3017-3038 ◽

Cited By ~ 138

Author(s):

Anna C. Fitch ◽

Joseph B. Olson ◽

Julie K. Lundquist ◽

Jimy Dudhia ◽

Alok K. Gupta ◽

...

Keyword(s):

Boundary Layer ◽

Wind Speed ◽

Kinetic Energy ◽

Wind Farm ◽

Weather Prediction ◽

Wind Farms ◽

Mean Flow ◽

Thrust Coefficient ◽

Near Surface ◽

Neutral Boundary Layer

Abstract A new wind farm parameterization has been developed for the mesoscale numerical weather prediction model, the Weather Research and Forecasting model (WRF). The effects of wind turbines are represented by imposing a momentum sink on the mean flow; transferring kinetic energy into electricity and turbulent kinetic energy (TKE). The parameterization improves upon previous models, basing the atmospheric drag of turbines on the thrust coefficient of a modern commercial turbine. In addition, the source of TKE varies with wind speed, reflecting the amount of energy extracted from the atmosphere by the turbines that does not produce electrical energy. Analyses of idealized simulations of a large offshore wind farm are presented to highlight the perturbation induced by the wind farm and its interaction with the atmospheric boundary layer (BL). A wind speed deficit extended throughout the depth of the neutral boundary layer, above and downstream from the farm, with a long wake of 60-km e-folding distance. Within the farm the wind speed deficit reached a maximum reduction of 16%. A maximum increase of TKE, by nearly a factor of 7, was located within the farm. The increase in TKE extended to the top of the BL above the farm due to vertical transport and wind shear, significantly enhancing turbulent momentum fluxes. The TKE increased by a factor of 2 near the surface within the farm. Near-surface winds accelerated by up to 11%. These results are consistent with the few results available from observations and large-eddy simulations, indicating this parameterization provides a reasonable means of exploring potential downwind impacts of large wind farms.

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Supplementing Oscat winds with Saral Altika observations for cyclone studies

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-8-1059-2014 ◽

2014 ◽

Vol XL-8 ◽

pp. 1059-1064 ◽

Cited By ~ 1

Author(s):

K. Niharika ◽

H. S. V. Usha Sundari ◽

A. V. V. Prasad ◽

E. V. S. Sita Kumari ◽

V. K. Dadhwal ◽

...

Keyword(s):

Remote Sensing ◽

Life Cycle ◽

Wind Speed ◽

Storm Surge ◽

Disaster Management ◽

Management Practices ◽

In Situ Observations ◽

Remote Sensing Techniques ◽

Microwave Sensors

Accurate prediction of life cycle of cyclone is very critical to the disaster management practices. Since the cyclones originate over the oceans where in situ observations are limited, we have to resort to the remote sensing techniques. Both optical and microwave sensors help studying the cyclones. While scatterometer provide wind vectors, altimeters can give only wind speed. In this paper we present how altimeter measurements can supplement the scatterometer observations in determining the radius of maximum winds (RMW). Sustained maximum winds, indicator for the intensity of the cyclone, are within the eye wall of a cyclone at a distance of RMW. This parameter is also useful in predicting right time of the storm surge. In this paper we used the wind speed estimations from AltiKa, an altimeter operating at Ka band.

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Traffic restrictions in Beijing during the Sino-African Summit 2006: aerosol size distribution and visibility compared to long-term in situ observations

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-8-12971-2008 ◽

2008 ◽

Vol 8 (4) ◽

pp. 12971-12998 ◽

Cited By ~ 1

Author(s):

Y. F. Cheng ◽

J. Heintzenberg ◽

B. Wehner ◽

Z. J. Wu ◽

M. Hu ◽

...

Keyword(s):

Wind Speed ◽

Fine Particles ◽

Ultrafine Particle ◽

Weather Conditions ◽

Particle Formation ◽

Control Measures ◽

Aerosol Size Distribution ◽

In Situ Observations

Abstract. Based on the long-term in-situ observations of aerosol particle number size distributions and meteorological parameters, the traffic restriction measures during the Sino-African Summit (4–6 November 2006) in Beijing, China have been found to be remarkably efficient in reducing the number concentration of aerosol particles, in particular Aitken and accumulation mode particles, and in improving the visibility. The influence of traffic restriction in Beijing on the particle concentrations differed for different particle sizes. More significant effects on fine particles with diameters ranging from 40 to 800 nm have been found. Based on statistical analysis of long-term observation, under comparable weather conditions, the source strength of the particles in Aitken and accumulation modes seemingly was reduced by 40–60% when the traffic restrictions were in place. It may be mainly due to the reduction of secondary particle formation. Our size-dependent aerosol data also indicate that measures led to reductions in particulate air pollution in the optically most important diameter range, whereas further vehicle control measures may lead to an increase in ultrafine particle formation from the gas phase if the condensational sink further decreased. Assuming that there were no traffic restrictions and with normal levels of the vehicle emissions, the visibilities during the Summit would have been lower by about 50%. The importance of the restrictions is highest when the wind speed is lower than 3 m s−1. The fact that over 95% cases with visual range lower than 5 km during 2004 to 2007 occurred when the local wind speed was lower than 3 m s−1 may suggest that future traffic restrictions will lead to significant improvements of visibility in Beijing.

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Deriving Arctic 2 m air temperatures over snow and ice from satellite surface temperature measurements

10.5194/tc-2021-38 ◽

2021 ◽

Author(s):

Pia Nielsen-Englyst ◽

Jacob L. Høyer ◽

Kristine S. Madsen ◽

Rasmus T. Tonboe ◽

Gorm Dybkjær ◽

...

Keyword(s):

Surface Temperature ◽

Arctic Region ◽

Satellite Observations ◽

The Arctic ◽

Clear Sky ◽

Ice Surface ◽

Air Temperatures ◽

In Situ Observations ◽

The Arctic Region

Abstract. The Arctic region is responding heavily to climate change, and yet, the air temperature of ice covered areas in the Arctic is heavily under-sampled when it comes to in situ measurements, resulting in large uncertainties in existing weather- and reanalysis products. This paper presents a method for estimating daily mean clear sky 2 meter air temperatures (T2m) in the Arctic from satellite observations of skin temperature, using the Arctic and Antarctic ice Surface Temperatures from thermal Infrared (AASTI) satellite dataset, providing spatially detailed observations of the Arctic. The method is based on a linear regression model, which has been tuned against in situ observations to estimate daily mean T2m based on clear sky satellite ice surface skin temperatures. The daily satellite derived T2m product includes estimated uncertainties and covers clear sky snow and ice surfaces in the Arctic region during the period 2000–2009, provided on a 0.25 degree regular latitude-longitude grid. Comparisons with independent in situ measured T2m show average biases of 0.30 °C and 0.35 °C and average root mean square errors of 3.47 °C and 3.20 °C for land ice and sea ice, respectively. The associated uncertainties are verified to be very realistic for both land ice and sea ice, using in situ observations. The reconstruction provides a much better spatial coverage than the sparse in situ observations of T2m in the Arctic, is independent of numerical weather prediction model input and it therefore provides an important supplement to simulated air temperatures to be used for assimilation or global surface temperature reconstructions. A comparison between in situ T2m versus T2m derived from satellite and ERA-Interim/ERA5 estimates shows that the T2m derived from satellite observations validate similar or better than ERA-Interim/ERA5 in the Arctic.

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Validation of satellite-based sea surface temperature products against in situ observations off the western coast of Sumatra

Scientific Reports ◽

10.1038/s41598-021-04156-0 ◽

2022 ◽

Vol 12 (1) ◽

Author(s):

Qoosaku Moteki

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

Sea Surface ◽

Optimal Interpolation ◽

Western Coast ◽

Mean Square ◽

Ensemble Spread ◽

In Situ Observations ◽

High Clouds

AbstractThis study validated the sea surface temperature (SST) datasets from the Group for High-Resolution SST Multi Product Ensemble (GMPE), National Oceanic and Atmospheric Administration (NOAA) Optimal Interpolation (OI) SST version 2 and 2.1 (OIv2 and OIv2.1), and Estimating the Circulation and Climate of the Ocean, Phase II (ECCO2) in the area off the western coast of Sumatra against in situ observations. Furthermore, the root mean square differences (RMSDs) of OIv2, OIv2.1, and ECCO2 were investigated with respect to GMPE, whose small RMSD < 0.2 K against in situ observations confirmed its suitability as a reference. Although OIv2 showed a large RMSD (1–1.5 K) with a significant negative bias, OIv2.1 (RMSD < 0.4 K) improved remarkably. In the average SST distributions for December 2017, the differences among the 4 datasets were significant in the areas off the western coast of Sumatra, along the southern coast of Java, and in the Indonesian inland sea. These results were consistent with the ensemble spread distribution obtained with GMPE. The large RMSDs of OIv2 corresponded to high clouds, and it was suggested that the change in the satellites used for SST estimation contributed to the improvement in OIv2.1.

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