The use of remote sensing in addressing scaling issues for numerical models of atmospheric processes

2002 ◽  
Author(s):  
E.F. LeDrew ◽  
D.G. Barber ◽  
T.N. Papakyriakou
Keyword(s):  
Remote Sensing ◽  
Numerical Models ◽  
Atmospheric Processes

scholarly journals Remote Sensing Supported Sea Surface pCO2 Estimation and Variable Analysis in the Baltic Sea

2021 ◽  
Vol 13 (2) ◽  
pp. 259
Author(s):  
Shuping Zhang ◽  
Anna Rutgersson ◽  
Petra Philipson ◽  
Marcus B. Wallin
Keyword(s):  
Remote Sensing ◽  
Baltic Sea ◽  
Mean Squared Error ◽  
Numerical Models ◽  
Mixed Layer Depth ◽  
Sea Surface ◽  
The Baltic Sea ◽  
Chl A ◽  
Marginal Seas ◽  
The Baltic

Marginal seas are a dynamic and still to large extent uncertain component of the global carbon cycle. The large temporal and spatial variations of sea-surface partial pressure of carbon dioxide (pCO2) in these areas are driven by multiple complex mechanisms. In this study, we analyzed the variable importance for the sea surface pCO2 estimation in the Baltic Sea and derived monthly pCO2 maps for the marginal sea during the period of July 2002–October 2011. We used variables obtained from remote sensing images and numerical models. The random forest algorithm was employed to construct regression models for pCO2 estimation and produce the importance of different input variables. The study found that photosynthetically available radiation (PAR) was the most important variable for the pCO2 estimation across the entire Baltic Sea, followed by sea surface temperature (SST), absorption of colored dissolved organic matter (aCDOM), and mixed layer depth (MLD). Interestingly, Chlorophyll-a concentration (Chl-a) and the diffuse attenuation coefficient for downwelling irradiance at 490 nm (Kd_490nm) showed relatively low importance for the pCO2 estimation. This was mainly attributed to the high correlation of Chl-a and Kd_490nm to other pCO2-relevant variables (e.g., aCDOM), particularly in the summer months. In addition, the variables’ importance for pCO2 estimation varied between seasons and sub-basins. For example, the importance of aCDOM were large in the Gulf of Finland but marginal in other sub-basins. The model for pCO2 estimate in the entire Baltic Sea explained 63% of the variation and had a root of mean squared error (RMSE) of 47.8 µatm. The pCO2 maps derived with this model displayed realistic seasonal variations and spatial features of sea surface pCO2 in the Baltic Sea. The spatially and seasonally varying variables’ importance for the pCO2 estimation shed light on the heterogeneities in the biogeochemical and physical processes driving the carbon cycling in the Baltic Sea and can serve as an important basis for future pCO2 estimation in marginal seas using remote sensing techniques. The pCO2 maps derived in this study provided a robust benchmark for understanding the spatiotemporal patterns of CO2 air-sea exchange in the Baltic Sea.

Modelling rain heterogeneities within an urban neighbourhood

2021 ◽  
Author(s):  
Karolin S. Ferner ◽  
K. Heinke Schlünzen ◽  
Marita Boettcher
Keyword(s):  
Wind Speed ◽  
Urban Areas ◽  
Climate Model ◽  
Numerical Models ◽  
Urban Climate ◽  
Cloud Microphysics ◽  
In Situ Measurements ◽  
Small Scale ◽  
Atmospheric Processes

<p>Urbanisation locally modifies the regional climate: an urban climate develops. For example, the average wind speed in cities is reduced, while the gustiness is increased. Buildings induce vertical winds, which influence the falling of rain. All these processes lead to heterogeneous patterns of rain at ground and on building surfaces. The small-scale spatial rain heterogeneities may cause discomfort for people. Moreover, non-uniform wetting of buildings affects their hydrothermal performance and durability of their facades.</p><p>Measuring rain heterogeneities between buildings is, however, nearly impossible. Building induced wind gusts negatively influence the representativeness of in-situ measurements, especially in densely urbanised areas. Weather radars are usually too coarse and, more importantly, require an unobstructed view over the domain and thus do not measure ground precipitation in urban areas. Consequently, researchers turn to numerical modelling in order to investigate small-scale precipitation heterogeneities between buildings.</p><p>In building science, numerical models are used to investigate rain heterogeneities typically focussing on single buildings and vertical facades. Only few studies were performed for more than a single building or with inclusion of atmospheric processes such as radiation or condensation. In meteorology, increasing computational power now allows the use of small-scale obstacle-resolving models resolving atmospheric processes while covering neighbourhoods.</p><p>In order to assess rain heterogeneities between buildings we extended the micro-scale and obstacle-resolving transport- and stream model MITRAS (Salim et al. 2019). The same cloud microphysics parameterisation as in its mesoscale sister model METRAS (Schlünzen et al., 2018) was applied and boundary conditions for cloud and rain water content at obstacle surfaces were introduced. MITRAS results are checked for plausibility using radar and in-situ measurements (Ferner et al., 2021). To our knowledge MITRAS is the first numerical urban climate model that includes rain and simulates corresponding processes.</p><p>Model simulations were initialised for various wind speeds and mesoscale rain rates to assess their influence on the heterogeneity of falling rain in a domain of 1.9 x 1.7 km² around Hamburg City Hall. We investigated how wind speed or mesoscale rain rate influence the precipitation patterns at ground and at roof level. Based on these results we assessed the height dependence of precipitation. First analyses show that higher buildings receive more rain on their roofs than lower buildings; the results will be presented in detail in our talk.</p><p>Ferner, K.S., Boettcher, M., Schlünzen, K.H. (2021): Modelling the heterogeneity of rain in an urban neighbourhood. Publication in preparation</p><p>Salim, M.H., Schlünzen, K.H., Grawe, D., Boettcher, M., Gierisch, A.M.U., Fock B.H. (2018): The microscale obstacle-resolving meteorological model MITRAS v2.0: model theory. Geosci. Model Dev., 11, 3427–3445, https://doi.org/10.5194/gmd-11-3427-2018.</p><p>Schlünzen, K.H., Boettcher, M., Fock, B.H., Gierisch, A.M.U., Grawe, D., and Salim, M. (2018): Scientific Documentation of the Multiscale Model System M-SYS. Meteorological Institute, Universität Hamburg. MEMI Technical Report 4</p>

scholarly journals A novel approach to characterize the variability in mass-Dimension relationships: results from MC3E

2018 ◽  
Author(s):  
Joseph A. Finlon ◽  
Greg M. McFarquhar ◽  
Stephen W. Nesbitt ◽  
Robert M. Rauber ◽  
Hugh Morrison ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Numerical Models ◽  
Radar Data ◽  
Chi Square ◽  
University Of North Dakota ◽  
Microphysical Properties ◽  
Novel Approach ◽  
Mass Dimension ◽  
Lower Temperature ◽  
B Coefficients

Abstract. Mass-dimension (m-D) relationships determining bulk microphysical properties such as total water content (TWC) and radar reflectivity factor (Z) from particle size distributions are used in both numerical models and remote sensing retrievals. The a and b coefficients representing m = aDb relationships, however, can vary significantly depending on meteorological conditions, particle habits, definition of particle maximum dimension, the probes used to obtain the data, techniques used to process the cloud probe data, and other unknown reasons. Thus, considering a range of a,b coefficients may be more applicable for use in numerical models and remote sensing retrievals. Microphysical data collected by two-dimensional optical array probes (OAPs) installed on the University of North Dakota Citation aircraft during the Mid-latitude Continental Convective Clouds Experiment (MC3E) were used in conjunction with TWC data from a Nevzorov probe and ground-based S-band radar data to determine a and b using a technique that minimizes the chi-square difference between TWC and Z derived from the OAPs and that directly measured by a TWC probe and radar. All a and b within a specified tolerance were regarded as equally plausible solutions. Of the 16 near-constant temperature flight legs analyzed during the 25 April, 20 May, and 23 May 2011 events, the derived surfaces of solutions on the first two days where the aircraft sampled stratiform cloud had a larger range in a and b for lower temperature environments that corresponded to less variability in N(D), TWC, and Z for a flight leg. Because different regions of the storm were sampled on 23 May, differences in the variability of N(D), TWC, and Z influenced the distribution of chi-square values in (a,b) phase space and the specified tolerance in a way that yielded 6.7 times fewer plausible solutions compared to the flight legs on the other dates. These findings show the importance of representing the variability in a,b coefficients for numerical modeling and remote sensing studies rather than assuming fixed values, as well as the need to further explore how these surfaces depend on environmental conditions in ice and mixed phase clouds.

Numerical modeling of mesospheric mountain wave propagation observed in Southern Argentina during the SOUTHTRAC campaign

2020 ◽  
Author(s):  
Tyler Mixa ◽  
Andreas Dörnbrack ◽  
Bernd Kaifler ◽  
Markus Rapp
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Numerical Models ◽  
Airborne Lidar ◽  
Tierra Del Fuego ◽  
Polar Night ◽  
Research Aircraft ◽  
Complex Propagation ◽  
The Antarctic

<p>We present numerical simulations of a deep orographic gravity wave (GW) event observed by the ALIMA airborne lidar on 11-12 September 2019 over Southern Argentina. The measurements are taken from the 2019 SOUTHTRAC Campaign, employing a comprehensive suite of remote sensing and in-situ instruments onboard the HALO research aircraft to study the stratospheric GW hotspot over Tierra del Fuego and the Antarctic Peninsula. Wind conditions on 11-12 September exhibit local and large-scale directional shear from the ground to the polar night jet, creating a complex propagation environment supporting multiple orientations of GW propagation and strong potential for local GW breaking and secondary GW generation. Using high resolution numerical models, we simulate the 3D evolution of the orographic GW field to analyze<span> the remote sensing and in-situ measurements from the event.</span></p>

scholarly journals A digital temperature atlas for the Norwegian Sea

2010 ◽  
Vol 67 (8) ◽  
pp. 1525-1537 ◽  
Author(s):  
Geir Ottersen
Keyword(s):  
Remote Sensing ◽  
Missing Values ◽  
Stock Assessment ◽  
Numerical Models ◽  
World Ocean ◽  
Fish Distribution ◽  
Temperature Fields ◽  
Norwegian Sea ◽  
Ambient Temperatures ◽  
June July

Abstract Ottersen, G. 2010. A digital temperature atlas for the Norwegian Sea. – ICES Journal of Marine Science, 67: 1525–1537. The first digital temperature atlas for the Norwegian Sea (Nordic Seas/GIN Sea) is described and examples of applications given. The atlas is intended mainly to make historical temperature values available to fisheries oceanographers, fisheries biologists, and stock assessment scientists in a structured, uniform format. It should also be of interest to physical oceanographers, climate researchers, and numerical modellers, and will be of relevance to remote-sensing analyses. The atlas, made freely available for scientific non-commercial purposes, is based on interpolation from 59 496 mainly Norwegian, Faroese, and Icelandic hydrographic stations. It consists of gridded temperature fields for the area 20°W–20°E 60–80°N, with a spatial resolution of 1/2° longitude by 1/3° latitude. It covers the quarters January–March, April–June, July–September, and October–December for each year from 1990 to 2007 at 28 depth levels from 0 to 500 m. Two versions of the atlas were produced, one based solely on actual data and one where cells with “missing” values were filled from World Ocean Atlas 05 climatology. Suggested applications include the mapping of horizontal fields and vertical sections, initiation or verification of numerical models, comparisons with SST values from remote sensing, calculations within any chosen latitude–longitude–depth box, and the estimation of the ambient temperatures fish experience when the atlas is used in conjunction with information on fish distribution.

scholarly journals EXTRACTING THE WATERLINE FROM LANDSAT IMAGES

2009 ◽  
Vol 12 (12) ◽  
pp. 52-58
Author(s):  
Thao Thi Phuong Pham ◽  
Duan Dinh Ho ◽  
To Van Dang
Keyword(s):  
Remote Sensing ◽  
Satellite Images ◽  
Numerical Models ◽  
Scientific Research ◽  
Remote Sensing And Gis ◽  
Landsat Images ◽  
Shoreline Changes ◽  
Remote Sensing Technology ◽  
Spectral Bands ◽  
Sensing Technology

Remote sensing technology nowadays is one of the most useful tools for scientific research in general and for oceanography in particular. From satellite images, the useful information such as waterline images can be extracte for a large region simultaneously. After tidal adjustments, the waterlines can be used as the observed shorelines which are important inputs for estimating shoreline changes by either using the integration of remote sensing and GIS or using numerical models. Based on the spectral bands of various Landsat images, the paper presents the methods to detect the waterlines in Phan Thiet region in the 40 years period using the images of 1973, 1976, 1990, and 2002 respectively. The extracted results relatively agree with the information of waterline from the images.

scholarly journals Indirect Validation of Ocean Remote Sensing Data via Numerical Model: An Example of Wave Heights from Altimeter

2020 ◽  
Vol 12 (16) ◽  
pp. 2627 ◽  
Author(s):  
Haoyu Jiang
Keyword(s):  
Remote Sensing ◽  
Numerical Model ◽  
Numerical Models ◽  
Remote Sensing Data ◽  
Validation Method ◽  
Wave Hindcast ◽  
Sensing Data ◽  
Data Pair ◽  
Wave Heights ◽  
Ocean Remote Sensing

Using numerical model outputs as a bridge, an indirect validation method for remote sensing data was developed to increase the number of effective collocations between remote sensing data to be validated and reference data. The underlying idea for this method is that the local spatial-temporal variability of specific parameters provided by numerical models can compensate for the representativeness error induced by differences of spatial-temporal locations of the collocated data pair. Using this method, the spatial-temporal window for collocation can be enlarged for a given error tolerance. To test the effectiveness of this indirect validation approach, significant wave height (SWH) data from Envisat were indirectly compared against buoy and Jason-2 SWHs, using the SWH gradient information from a numerical wave hindcast as a bridge. The results indicated that this simple indirect validation method is superior to “direct” validation.

scholarly journals Asian Dust properties investigated by multi-instruments

2019 ◽  
Vol 99 ◽  
pp. 02003
Author(s):  
Janbai Nee
Keyword(s):  
Remote Sensing ◽  
Dust Cloud ◽  
Asian Dust ◽  
Dust Storms ◽  
Dust Particles ◽  
Atmospheric Processes ◽  
Aerosol Sources ◽  
Dust Events ◽  
Aerosol Cloud Interactions ◽  
Lidar Observations

Dust and many types of aerosols are major pollutants significantly affecting the environment in the East Asia. To identify and classify various types of aerosols is a challenge. In Taiwan and nearby areas, Asian Dust mainly arrive in spring with an average of about 5 dust storms each year. They usually come with some other aerosol sources, therefore it is important to identify these aerosols and their properties. In this paper, we report studying of dust aerosols by using several ground-based and remote sensing measurements. The AERONET data is used to find optical properties of aerosols in 2008-2012. The lidar observations can investigate further properties and atmospheric processes for specific dust events, including observations of aerosol-cloud interactions. These combined with model or space observations can help us to understand long range dust particles transported to distant areas and their interaction with weather systems. A real time case of observation of dust-cloud interaction is provided.

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