Assessing the Impact of Land Use and Land Cover Data Representation on Weather Forecast Quality: A Case Study in Central Mexico

In atmospheric modeling, an accurate representation of land cover is required because such information impacts water and energy budgets and, consequently, the performance of models in simulating regional climate. This study analyzes the impact of the land cover data on an operational weather forecasting system using the Weather Research and Forecasting (WRF) model for central Mexico, with the aim of improving the quality of the operative forecast. Two experiments were conducted using different land cover datasets: a United States Geological Survey (USGS) map and an updated North American Land Change Monitoring System (NALCMS) map. The experiments were conducted as a daily 120 h forecast for each day of January, April, July, and September of 2012, and the near-surface temperature, wind speed, and hourly precipitation were analyzed. Both experiments were compared with observations from meteorological stations. The statistical analysis of this study showed that wind speed and near-surface temperature prediction may be further improved with the updated and more accurate NALCMS dataset, particularly in the forecast covering 48 to 72 h. The Root Mean Square Error (RMSE) of the average wind speed reached a maximum reduction of up to 1.2 m s−1, whereas for the near-surface temperature there was a reduction of up to 0.6 °C. The RMSE of the average hourly precipitation was very similar between both experiments, however the location of precipitation was modified.

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The impact of shallow stratification on air-sea CO2 flux in the summer Arctic Ocean

10.5194/egusphere-egu21-715 ◽

2021 ◽

Author(s):

Yuanxu Dong ◽

Dorothee Bakker ◽

Thomas Bell ◽

Peter Liss ◽

Ian Brown ◽

...

Keyword(s):

Surface Water ◽

Wind Speed ◽

Sea Ice ◽

Surface Temperature ◽

Arctic Ocean ◽

The Arctic ◽

Near Surface ◽

Ice Melt ◽

The Arctic Ocean ◽

The Impact

Air-sea carbon dioxide (CO2) flux is often indirectly estimated by the bulk method using the in-situ air-sea difference in CO2 fugacity and a wind speed dependent parameterisation of the gas transfer velocity (K). In the summer, sea-ice melt in the Arctic Ocean generates strong shallow stratification with significant gradients in temperature, salinity, dissolved inorganic carbon (DIC) and alkalinity (TA), and thus a near-surface CO2 fugacity &#160;(fCO2w) gradient. This gradient can cause an error in bulk air-sea CO2 flux estimates when the fCO2w is measured by the ship&#8217;s underway system at ~5 m depth. Direct air-sea CO2 flux measurement by eddy covariance (EC) is free from the impact of shallow stratification because the EC CO2 flux does not rely on a fCO2w measurement. In this study, we use summertime EC flux measurements from the Arctic Ocean to back-calculate the sea surface fCO2w and temperature and compare them with the underway measurements. We show that the EC air-sea CO2 flux agrees well with the bulk flux in areas less likely to be influenced by ice melt (salinity > 32). However, in regions with salinity less than 32, the underway fCO2w is higher than the EC estimate of surface fCO2w and thus the bulk estimate of ocean CO2 uptake is underestimated. The fCO2w difference can be partly explained by the surface to sub-surface temperature difference. The EC estimate of surface temperature is lower than the sub-surface water temperature and this difference is wind speed-dependent. Upper-ocean salinity gradients from CTD profiles suggest likely difference in DIC and TA concentrations between the surface and sub-surface water. These DIC and TA gradients likely explain much of the near-surface fCO2w gradient. Accelerating summertime loss of sea ice results in additional meltwater, which enhances near-surface stratification and increases the uncertainty of bulk air-sea CO2 flux estimates in polar regions.

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Idealized large-eddy simulations of stratocumulus advecting over cold water. Part 1: Boundary layer decoupling

Journal of the Atmospheric Sciences ◽

10.1175/jas-d-21-0108.1 ◽

2021 ◽

Author(s):

Youtong Zheng ◽

Haipeng Zhang ◽

Daniel Rosenfeld ◽

Seoung-Soo Lee ◽

Tianning Su ◽

...

Keyword(s):

Boundary Layer ◽

Surface Temperature ◽

Cold Water ◽

Temperature Inversion ◽

Atmospheric Modeling ◽

Sea Surface ◽

Entrainment Rate ◽

Surface Cooling ◽

Near Surface ◽

Large Eddy

AbstractWe explore the decoupling physics of a stratocumulus-topped boundary layer (STBL) moving over cooler water, a situation mimicking the warm air advection (WADV). We simulate an initially well-mixed STBL over a doubly periodic domain with the sea surface temperature decreasing linearly over time using the System for Atmospheric Modeling large-eddy model. Due to the surface cooling, the STBL becomes increasingly stably stratified, manifested as a near-surface temperature inversion topped by a well-mixed cloud-containing layer. Unlike the stably stratified STBL in cold air advection (CADV) that is characterized by cumulus coupling, the stratocumulus deck in the WADV is unambiguously decoupled from the sea surface, manifested as weakly negative buoyancy flux throughout the sub-cloud layer. Without the influxes of buoyancy from the surface, the convective circulation in the well-mixed cloud-containing layer is driven by cloud-top radiative cooling. In such a regime, the downdrafts propel the circulation, in contrast to that in CADV regime for which the cumulus updrafts play a more determinant role. Such a contrast in convection regime explains the difference in many aspects of the STBLs including the entrainment rate, cloud homogeneity, vertical exchanges of heat and moisture, and lifetime of the stratocumulus deck, with the last being subject to a more thorough investigation in part 2. Finally, we investigate under what conditions a secondary stratus near the surface (or fog) can form in the WADV. We found that weaker subsidence favors the formation of fog whereas a more rapid surface cooling rate doesn’t.

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Impact of Multitemporal Land Use and Land Cover Change on Land Surface Temperature Due to Urbanization in Hefei City, China

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10120809 ◽

2021 ◽

Vol 10 (12) ◽

pp. 809

Author(s):

Jing Sun ◽

Suwit Ongsomwang

Keyword(s):

Land Use ◽

Land Cover ◽

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Urban Areas ◽

Decomposition Analysis ◽

Lulc Change ◽

Main Components ◽

The Impact

Land surface temperature (LST) is an essential parameter in the climate system whose dynamics indicate climate change. This study aimed to assess the impact of multitemporal land use and land cover (LULC) change on LST due to urbanization in Hefei City, Anhui Province, China. The research methodology consisted of four main components: Landsat data collection and preparation; multitemporal LULC classification; time-series LST dataset reconstruction; and impact of multitemporal LULC change on LST. The results revealed that urban and built-up land continuously increased from 2.05% in 2001 to 13.25% in 2020. Regarding the impact of LULC change on LST, the spatial analysis demonstrated that the LST difference between urban and non-urban areas had been 1.52 K, 3.38 K, 2.88 K and 3.57 K in 2001, 2006, 2014 and 2020, respectively. Meanwhile, according to decomposition analysis, regarding the influence of LULC change on LST, the urban and built-up land had an intra-annual amplitude of 20.42 K higher than other types. Thus, it can be reconfirmed that land use and land cover changes due to urbanization in Hefei City impact the land surface temperature.

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The Impact of Contributor Confidence, Expertise and Distance on the Crowdsourced Land Cover Data Quality

GI_Forum 2014 – Geospatial Innovation for Society ◽

10.1553/giscience2014s309 ◽

2015 ◽

Author(s):

Keyword(s):

Land Cover ◽

Data Quality ◽

Land Cover Data ◽

The Impact

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The Impact of Land Cover Data on Rainfall-Runoff Prediction Using an Entity-Aware-LSTM

10.5194/egusphere-egu21-1136 ◽

2021 ◽

Author(s):

Sebastian Drost ◽

Fabian Netzel ◽

Andreas Wytzisk-Ahrens ◽

Christoph Mudersbach

Keyword(s):

Land Cover ◽

Short Term Memory ◽

Land Cover Changes ◽

Rainfall Runoff ◽

Corine Land Cover ◽

Land Cover Data ◽

Modelling Problem ◽

Long Short Term Memory ◽

The Impact ◽

Process Based Models

The application of Deep Learning methods for modelling rainfall-runoff have reached great advances in the last years. Especially, long short-term memory (LSTM) networks have gained enhanced attention for time-series prediction. The architecture of this special kind of recurrent neural network is optimized for learning long-term dependencies from large time-series datasets. Thus, different studies proved the applicability of LSTM networks for rainfall-runoff predictions and showed, that they are capable of outperforming other types of neural networks (Hu et al., 2018).Understanding the impact of land-cover changes on rainfall-runoff dynamics is an important task. Such a hydrological modelling problem typically is solved with process-based models by varying model-parameters related to land-cover-incidents&#160;at different points in time. Kratzert et al. (2019) proposed an adaption of the standard LSTM architecture, called Entity-Aware-LSTM (EA-LSTM), which can take static catchment attributes as input features to overcome the regional modelling problem and provides a promising approach for similar use cases. Hence, our contribution aims to analyse the suitability of EA-LSTM for assessing the effect of land-cover changes.In different experimental setups, we train standard LSTM and EA-LSTM networks for multiple small subbasins, that are associated to the Wupper region in Germany. Gridded daily precipitation data&#160;from the REGNIE dataset (Rauthe et al., 2013), provided by the German Weather Service&#160;(DWD),&#160;is used as model input to predict the daily discharge for each subbasin.&#160;For training the EA-LSTM we use land cover information from the European CORINE Land Cover (CLC) inventory as static input features. The CLC inventory includes Europe-wide timeseries of land cover in 44 classes as well as land cover changes for different time periods (B&#252;ttner, 2014). The percentage proportion of each land cover class within a subbasin serves as static input features. To evaluate the impact of land cover data on rainfall-runoff prediction, we compare the results of the EA-LSTM with those of the standard LSTM considering different statistical measures as well as the Nash&#8211;Sutcliffe ef&#64257;ciency (NSE).In addition, we test the ability of the EA-LSTM to outperform physical process-based models. For this purpose, we utilize existing and calibrated hydrological models within the Wupper basin to simulate discharge for each subbasin. Finally, performance metrics of the calibrated model are used as benchmarks for assessing the performance of the EA-LSTM model.ReferencesB&#252;ttner, G. (2014). CORINE Land Cover and Land Cover Change Products. In: Manakos & M. Braun (Hrsg.), Land Use and Land Cover Mapping in Europe (Bd. 18, S. 55&#8211;74). Springer Netherlands. https://doi.org/10.1007/978-94-007-7969-3_5Hu, C., Wu, Q., Li, H., Jian, S., Li, N., & Lou, Z. (2018). Deep Learning with a Long Short-Term Memory Networks Approach for Rainfall-Runoff Simulation. Water, 10(11), 1543. https://doi.org/10.3390/w10111543Kratzert, F., Klotz, D., Shalev, G., Klambauer, G., Hochreiter, S., & Nearing, G. (2019). Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets. Hydrology and Earth System Sciences, 23(12), 5089&#8211;5110. https://doi.org/10.5194/hess-23-5089-2019Rauthe, M, Steiner, H, Riediger, U, Mazurkiewicz, A &Gratzki, A (2013): A Central European precipitation climatology &#8211; Part I: Generation and validation of a high-resolution gridded daily data set (HYRAS), Meteorologische Zeitschrift, Vol 22, No 3, 235&#8211;256. https://doi.org/10.1127/0941-2948/2013/0436

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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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Field measurements of windbreak effects on airflow, turbulent exchanges and microclimates

Australian Journal of Experimental Agriculture ◽

10.1071/ea02004 ◽

2002 ◽

Vol 42 (6) ◽

pp. 665 ◽

Cited By ~ 19

Author(s):

H. A. Cleugh

Keyword(s):

Wind Speed ◽

Water Vapour ◽

Incident Angle ◽

Surface Wind ◽

Field Measurements ◽

Experimental Program ◽

Near Surface ◽

Speed Reduction ◽

Temperature And Humidity ◽

The Impact

While there has been considerable research into airflow around windbreaks, the interaction of this airflow with the exchanges of heat and water vapour has received far less attention. Yet, the effects of windbreaks on microclimates, water use and agricultural productivity depend, in part, on this interaction. A field and wind tunnel experimental program was conducted to quantify the effects of windbreaks on microclimates and evaporation fluxes. This paper describes the field measurements, which were conducted over a 6-week period at a tree windbreak site located in undulating terrain in south-east Australia. The expected features of airflow around porous windbreaks were observed despite the less than ideal nature of the site. As predicted from theory, the air temperature and humidity were elevated, by day, in the quiet zone and the location of the peak increase in temperature and humidity coincided with the location of the minimum wind speed. However, this increase in temperature and humidity was small in size and restricted to the zone within 10 windbreak heights (H) of the windbreak. This pattern contrasts with that for the near surface wind speeds, which were reduced by up to 80% in a sheltered zone that extended from 5 H upwind to over 25 H downwind of the windbreak. Similar differences were found between the turbulent scalar (heat, water vapour) and velocity terms. While both are reduced in the quiet zone, the turbulent scalar terms near the surface were substantially enhanced at the location where the wake zone begins. Here the mean wind speed is reduced by 50% and the turbulent velocity terms return to their upwind values. Wind speed reductions varied linearly with [cos (90 – α)], where α is the incident angle of the wind, for sites located 6 H downwind. This means that the spatial pattern of wind speed reduction applies to all wind directions, provided that distance downwind is expressed in terms of streamwise distance. However, shelter in the near-break region is slightly increased as the wind blows more obliquely towards the windbreak. The atmospheric demand in the quiet zone was reduced when the humidity of the upwind air was low. In such conditions, windbreaks can 'protect' growing crops from the impact of dry air with high atmospheric demand. The corollary is that in humid conditions, the atmospheric demand in the quiet zone can be increased as a result of shelter.

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