Use of Sensor Imagery Data for Surface Boundary Conditions in Regional Climate Modeling

Mesoscale climate and hydrology modeling studies have increased in sophistication and are being run at increasingly higher resolutions. Data resolution sufficiently finer than that of the computational model is required not only to support sophisticated linkages and process interactions at small scales but to assess their cumulative impact at larger scales. The global distributions at fine spatial and temporal scales can be described by means of various senor imagery data collected through remote sensing techniques, sensor image and photo programs, scanning and digitizing skills for existing maps, etc. The availability of global sensor imagery maps facilitates assimilation in land surface models to account for terrestrial dynamics. This study focuses on the use of global imagery data for development and construction of surface boundary conditions (SBCs) specifically designed for mesoscale regional climate model (RCM) applications. The several SBCs are currently presented in a RCM domain for the continent of Asia at 30-km spacing by using sensor imagery data. Geographic Information System (GIS) software application tools are mainly used to convert data information from various raw data onto RCM-specific grids. The raw data sources and processing procedures are elaborated in detail, by which the SBCs can be readily constructed for any specific RCM domain anywhere in the world

Calculation of wind-driven surface currents in the North Atlantic Ocean

Calculations to simulate the wind driven near surface currents of the North Atlantic Ocean are described. The primitive equations were integrated on a finite difference grid with a horizontal resolution of 2.5 deg in longitude and latitude. The model ocean was homogeneous with a uniform depth of 100 m and with five levels in the vertical direction. A form of the rigid-lid approximation was applied. Generally, the computed surface current patterns agreed with observed currents. The development of a subsurface equatorial countercurrent was observed

Meteorological application of Apollo photography Final report

Development of meteorological information and parameters based on cloud photographs taken during Apollo 9 fligh

Coupled atmosphere-wildland fire modeling with WRF-Fire

We describe the physical model, numerical algorithms, and software structure of WRF-Fire. WRF-Fire consists of a fire-spread model, implemented by the level-set method, coupled with the Weather Research and Forecasting model. In every time step, the fire model inputs the surface wind, which drives the fire, and outputs the heat flux from the fire into the atmosphere, which in turn influences the atmosphere. The level-set method allows submesh representation of the burning region and flexible implementation of various ignition modes. WRF-Fire is distributed as a part of WRF and it uses the WRF parallel infrastructure for parallel computing.Comment: Version 3.3, 41 pages, 2 tables, 12 figures. As published in Discussions, under review for Geoscientific Model Developmen

Simple I/O-efficient flow accumulation on grid terrains

The flow accumulation problem for grid terrains takes as input a matrix of flow directions, that specifies for each cell of the grid to which of its eight neighbours any incoming water would flow. The problem is to compute, for each cell c, from how many cells of the terrain water would reach c. We show that this problem can be solved in O(scan(N)) I/Os for a terrain of N cells. Taking constant factors in the I/O-efficiency into account, our algorithm may be an order of magnitude faster than the previously known algorithm that is based on time-forward processing and needs O(sort(N)) I/Os.Comment: This paper is an exact copy of the paper that appeared in the abstract collection of the Workshop on Massive Data Algorithms, Aarhus, 200

Remote sensing observatory validation of surface soil moisture using Advanced Microwave Scanning Radiometer E, Common Land Model, and ground based data: Case study in SMEX03 Little River Region, Georgia, U.S.

Optimal soil moisture estimation may be characterized by intercomparisons among remotely sensed measurements, ground‐based measurements, and land surface models. In this study, we compared soil moisture from Advanced Microwave Scanning Radiometer E (AMSR‐E), ground‐based measurements, and a Soil‐Vegetation‐Atmosphere Transfer (SVAT) model for the Soil Moisture Experiments in 2003 (SMEX03) Little River region, Georgia. The Common Land Model (CLM) reasonably replicated soil moisture patterns in dry down and wetting after rainfall though it had modest wet biases (0.001–0.054 m3/m3) as compared to AMSR‐E and ground data. While the AMSR‐E average soil moisture agreed well with the other data sources, it had extremely low temporal variability, especially during the growing season from May to October. The comparison results showed that highest mean absolute error (MAE) and root mean squared error (RMSE) were 0.054 and 0.059 m3/m3 for short and long periods, respectively. Even if CLM and AMSR‐E had complementary strengths, low MAE (0.018–0.054 m3/m3) and RMSE (0.023–0.059 m3/m3) soil moisture errors for CLM and soil moisture low biases (0.003–0.031 m3/m3) for AMSR‐E, care should be taken prior to employing AMSR‐E retrieved soil moisture products directly for hydrological application due to its failure to replicate temporal variability. AMSR‐E error characteristics identified in this study should be used to guide enhancement of retrieval algorithms and improve satellite observations for hydrological sciences

Uncertainty quantification of satellite precipitation estimation and Monte Carlo assessment of the error propagation into hydrologic response

The aim of this paper is to foster the development of an end-to-end uncertainty analysis framework that can quantify satellite-based precipitation estimation error characteristics and to assess the influence of the error propagation into hydrological simulation. First, the error associated with the satellite-based precipitation estimates is assumed as a nonlinear function of rainfall space-time integration scale, rain intensity, and sampling frequency. Parameters of this function are determined by using high-resolution satellite-based precipitation estimates and gauge-corrected radar rainfall data over the southwestern United States. Parameter sensitivity analysis at 16 selected 5° × 5° latitude-longitude grids shows about 12-16% of variance of each parameter with respect to its mean value. Afterward, the influence of precipitation estimation error on the uncertainty of hydrological response is further examined with Monte Carlo simulation. By this approach, 100 ensemble members of precipitation data are generated, as forcing input to a conceptual rainfall-runoff hydrologic model, and the resulting uncertainty in the streamflow prediction is quantified. Case studies are demonstrated over the Leaf River basin in Mississippi. Compared with conventional procedure, i.e., precipitation estimation error as fixed ratio of rain rates, the proposed framework provides more realistic quantification of precipitation estimation error and offers improved uncertainty assessment of the error propagation into hydrologic simulation. Further study shows that the radar rainfall-generated streamflow sequences are consistently contained by the uncertainty bound of satellite rainfall generated streamflow at the 95% confidence interval. Copyright 2006 by the American Geophysical Union

WUDAPT: Facilitating advanced urban canopy modeling for weather, climate and air quality applications

Environmental issues and impacts to society will be exacerbated with increased population, diminishing resources and the prospects for extreme weather events and climate changes. Current community-based models available for weather, climate and air quaity applications are powerful state-of-science modeling systems, which, with careful considerations, can be employed to address the impact of these issues fo urban areas. Given the complex and high degree of spatial inhomogeneity of the underlying surface area we will review mesh size, appropriate multi-scale science and morphological descriptions and their data requirements including unique city specific gridded morphology and material composition for their forecasting and climate applications. For this presentation, we discuss, describe and show examples from an ongoing but preliminary prototypic collaborative effort, whose design bases is to provide the experience and recommendations toward extending the scope of the National Urban Database and Access Portal Tools (NUDAPT) to worldwide coverage (WUDAPT). WUDAPT would thus provide requisite gridded data for urban applications of advanced forecast and climate models throughout the world. Strategically, the prototypic efforts will be designed to provide proven protocols for the facilitaton of the data gathering and processing based on available remote sensing and ground-based sampling. Tactically, we employ an iterative approach first obtaining coarse gridded Local Climate Zone (LCZ) classification derived from available Web-based products such as Google-Earth, and Landsat satellite magery. Further sub-class discretization of LCZs and the application of GeoWiki technology facilitates further refinements and ground truthing to yield the desired gridded building morphological distribution parameters and their material composition. Local experts would be encouraged to become involved to ensure factors unique to their area in the world would be incorporated. Finally, given that model applications may require data with different grid resolution we present an outline that employs the new and powerful Multiple Resolution Analyses scheme that can address this need within the scope of WUDAPT

Mapping the Energy Cascade in the North Atlantic Ocean: The Coarse-graining Approach

This is the final version of the article. Available from AMS via the DOI in this record.A coarse-graining framework is implemented to analyze nonlinear processes, measure energy transfer rates and map out the energy pathways from simulated global ocean data. Traditional tools to measure the energy cascade from turbulence theory, such as spectral flux or spectral transfer rely on the assumption of statistical homogeneity, or at least a large separation between the scales of motion and the scales of statistical inhomogeneity. The coarse-graining framework allows for probing the fully nonlinear dynamics simultaneously in scale and in space, and is not restricted by those assumptions. This paper describes how the framework can be applied to ocean flows. Energy transfer between scales is not unique due to a gauge freedom. Here, it is argued that a Galilean invariant subfilter scale (SFS) flux is a suitable quantity to properly measure energy scale-transfer in the Ocean. It is shown that the SFS definition can yield answers that are qualitatively different from traditional measures that conflate spatial transport with the scale-transfer of energy. The paper presents geographic maps of the energy scale-transfer that are both local in space and allow quasi-spectral, or scale-by-scale, dynamics to be diagnosed. Utilizing a strongly eddying simulation of flow in the North Atlantic Ocean, it is found that an upscale energy transfer does not hold everywhere. Indeed certain regions, near the Gulf Stream and in the Equatorial Counter Current have a marked downscale transfer. Nevertheless, on average an upscale transfer is a reasonable mean description of the extra-tropical energy scale-transfer over regions of O(10^3) kilometers in size.Financial support was provided by IGPPS at Los Alamos National Laboratory (LANL) and NSF grant OCE-1259794. HA was also supported through DOE grants de-sc0014318, de-na0001944, and the LANL LDRD program through project number 20150568ER. MH was also supported through the HiLAT project of the Regional and Global Climate Modeling program of the DOE’s Office of Science, and GKV was also supported by NERC, the Marie Curie Foundation and the Royal Society (Wolfson Foundation). This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231