Radiation Fog Prediction Using a Simple Numerical Model

—A simple one-dimensional numerical-analytical model was developed by Meyer and Rao (1995) to predict the onset of radiation fog. The model computes radiative cooling and turbulent diffusion of heat and vapor through the lower boundary layer and produces heat and vapor fluxes at the soil–atmosphere interface. The model is designed for Air Force forecasters who have access to a personal computer, an early evening surface observation of the dry bulb and dewpoint temperature, wind speed, the lapse rate in the upper boundary layer, and the previous 24-h precipitation amount. These initial data are used to predict the diurnal variation of the dry bulb and dewpoint temperatures at 10 m above the surface. In accordance with conventional synoptic observing practices, fog is defined as a restriction of the surface visibility generally to less than 1000 m. Fog is assumed to occur in the model predictions when the dewpoint depression falls to less than 1°C. Observations, from several Air Force bases for selected days when fog was observed to occur, were used to test the model. The present model with default parameters appears to predict the onset of fog slightly ahead of its occurrence. Better verification results are expected when site-relevant parameters are used in model predictions.     

Numerical Modeling of Ice Jams

A numerical model is proposed to describe the formation of ice jams under the effect of a release wave.     

Forecasting Natural Aquifer Discharge Using a Numerical Model and Convolution

If the nature of groundwater sources and sinks can be determined or predicted, the data can be used to forecast natural aquifer discharge. We present a procedure to forecast the relative contribution of individual aquifer sources and sinks to natural aquifer discharge. Using these individual aquifer recharge components, along with observed aquifer heads for each January, we generate a 1‐year, monthly spring discharge forecast for the upcoming year with an existing numerical model and convolution. The results indicate that a forecast of natural aquifer discharge can be developed using only the dominant aquifer recharge sources combined with the effects of aquifer heads (initial conditions) at the time the forecast is generated. We also estimate how our forecast will perform in the future using a jackknife procedure, which indicates that the future performance of the forecast is good (Nash‐Sutcliffe efficiency of 0.81). We develop a forecast and demonstrate important features of the procedure by presenting an application to the Eastern Snake Plain Aquifer in southern Idaho.     

Space Weather Forecasting and Supporting Research in the USA

In the United State, scientific research in space weather is funded by several Government Agencies including the National Science Foundation (NSF) and the National Aeronautics and Space Agency (NASA). For civilian and commercial purposes, space weather forecast is done by the Space Weather Prediction Center (SWPC) of the National Oceanic and Atmospheric Administration (NOAA). Observational data for modeling come from the network of groundbased observatories funded via various sources, as well as from the instruments on spacecraft. Numerical models used in forecast are developed in framework of individual research projects. The article provides a brief review of current state of space weather-related research and forecasting in the USA.     

Toward Improved Solar Irradiance Forecasts: a Simulation of Deep Planetary Boundary Layer with Scattered Clouds Using the Weather Research and Forecasting Model

Accurate forecasts of solar irradiance are required for electric utilities to economically integrate substantial amounts of solar power into their power generation portfolios. A common failing of numerical weather models is the prediction of scattered clouds at the top of deep PBL which are generally difficult to be resolved due to complicated processes in the planetary boundary layer. We improved turbulence parameterization for better predicting solar irradiance during the scattered clouds’ events using the Weather Research and Forecasting model. Sensitivity tests show that increasing the exchange coefficient leads to enhanced vertical mixing and a deeper mixed layer. At the top of mixed layer, an adiabatically ascending air parcel achieved the water vapor saturation and finally scattered cloud is generated.     

The Impact of Vertical Resolution in the Explicit Numerical Forecasting of Radiation Fog: A Case Study

Numerical experiments are performed with a comprehensive one-dimensional boundary layer/fog model to assess the impact of vertical resolution on explicit model forecasts of an observed fog layer. Two simulations were performed, one using a very high resolution and another with a vertical grid typical of current high-resolution mesoscale models. Both simulations were initialized with the same profiles, derived from observations from a fog field experiment. Significant differences in the onset and evolution of fog were found. The results obtained with the high-resolution simulation are in overall better agreement with available observations. The cooling rate before the appearance of fog is better represented, while the evolution of the liquid water content within the fog layer is more realistic. Fog formation is delayed in the low resolution simulation, and the water content in the fog layer shows large-amplitude oscillations. These results show that the numerical representation of key thermo-dynamical processes occurring in fog layers is significantly altered by the use of a grid with reduced vertical resolution.     

The Local Ensemble Transform Kalman Filter with the Weather Research and Forecasting Model: Experiments with Real Observations

The local ensemble transform Kalman filter (LETKF) is implemented with the Weather Research and Forecasting (WRF) model, and real observations are assimilated to assess the newly-developed WRF-LETKF system. The WRF model is a widely-used mesoscale numerical weather prediction model, and the LETKF is an ensemble Kalman filter (EnKF) algorithm particularly efficient in parallel computer architecture. This study aims to provide the basis of future research on mesoscale data assimilation using the WRF-LETKF system, an additional testbed to the existing EnKF systems with the WRF model used in the previous studies. The particular LETKF system adopted in this study is based on the system initially developed in 2004 and has been continuously improved through theoretical studies and wide applications to many kinds of dynamical models including realistic geophysical models. Most recent and important improvements include an adaptive covariance inflation scheme which considers the spatial and temporal inhomogeneity of inflation parameters. Experiments show that the LETKF successfully assimilates real observations and that adaptive inflation is advantageous. Additional experiments with various ensemble sizes show that using more ensemble members improves the analyses consistently.     

Implementation of a Single-Column Model for Fog and Low Cloud Forecasting at Central-Spanish Airports

Operations at Central-Spanish airports are often, especially in winter, affected by visibility reduction. The Instituto Nacional de Meteorología (INM), the Spanish Weather Service, has developed a single-column model (SCM) in order to improve short-term forecasts of fog, visibility and low-clouds. The SCM, called H1D, is a one-dimensional version of the HIRLAM limited-area model. It is operationally run for three airports in the region: Madrid-Barajas, Almagro and Albacete-Los Llanos. Since SCMs cannot deal with horizontal heterogeneity, the terms that depend on the horizontal structure of the atmosphere are estimated from the outputs of the three-dimensional (3-D) model and introduced into the SCM as external forcings. The systematic analysis of the meteorological situations has evidenced the existence of a close relationship between fog formation and the presence of drainage winds in the region. Since the 3-D model docs not have the necessary resolution to correctly simulate the main features of the drainage flow caused by the complex topography in the proximity of Madrid-Barajas, it cannot provide the SCM with the correct forcings. This problem has been partially overcome through the introduction of a module that, under certain conditions, substitutes the values computed from the 3-D model outputs by others that are based on a conceptual model of the phenomenon and have been empirically derived from climatological knowledge. This module improves the H1D verification scores for the basic meteorological variables—wind, temperature and humidity—and reduces the false alarm rate in fog forecast.     

Note on the 1964 Alaska Tsunami Generation by Horizontal Displacements of Ocean Bottom. Numerical Modeling of the Runup in Chenega Cove, Alaska

A numerical model of the wave dynamics in Chenega Cove, Alaska during the historic M _w 9.2 megathrust earthquake is presented. During the earthquake, locally generated waves of unknown origin were identified at the village of Chenega, located in the western part of Prince William Sound. The waves appeared shortly after the shaking began and swept away most of the buildings while the shaking continued. We model the tectonic tsunami in Chenega Cove assuming different tsunami generation processes. Modeled results are compared with eyewitness reports and an observed runup. Results of the numerical experiments let us claim the importance of including both vertical and horizontal displacement into the 1964 tsunami generation process. We also present an explanation for the fact that arrivals of later waves in Chenega were unnoticed.     

Deep Radiation Fog in a Wide Closed Valley: Study by Numerical Modeling and Remote Sensing

The Ebro river basin, in the northeastern part of the Iberian Peninsula in Europe, very often experiences radiation fog episodes in winter that can last for several days. The impact on human activities is high, especially on road and air transportation. The installation in July 2009 of a WindRASS in the area, which is able to work in the presence of fog, now allows inspecting the vertical structure of the temperature and wind profiles across the roughly 300-m-thick fog layer. We present a case study of a long-lasting (60 h) deep radiation fog that took place in December 2009 to obtain a deeper understanding of the dynamic processes governing such persistent fog. Field observations of vertical profiles of temperature, wind and turbulent kinetic energy are compared with a high-resolution mesoscale simulation, satellite imagery of fog distribution and observations taken in the area to understand why the fog is so persistent and how it dissipates only for a short period in the afternoon despite intermittent turbulence within the fog deck. The confinement of the fog inside a practically closed basin allows us to study the relevant physical processes in the establishment and subsequent evolution of the fog episode using a limited-area mesoscale model. The contribution of the WindRASS measurements allowed us to validate the numerical simulations, particularly inspecting the role of turbulence that can link the bottom and top of the fog through moderate episodic mixing. The fog layer has very weak winds inside, but is well mixed and experiences intermittent top-bottom turbulence generated in its upper part by convection due to radiative cooling and by wind shear due to the topographically generated flows that blow just above the top of the fog.     

A Review of some Recent Radiation Fog Prediction Studies and the Results of Integrating a Simple Numerical Model to Predict Radiation Fog over Brunei

— Several radiation fog studies with emphasis on numerical simulation and prediction are reviewed. One of the earliest attempts started with a given surface diurnal variation of temperature and water vapor, and concluded by forecasting the onset of saturation at various levels; thus fog, by examining the spread of temperature and moisture in the vertical. The one-dimensional (1-D) models are still popular. Some of the recent numerical simulations use more than 100 levels in the vertical and treat various kinds of vegetation, aerosols, and soils with moisture contents. Some also employ a mesoscale model in conjunction with a 1-D model to consider the advective effects. In the following a simple 1-D numerical model was used to predict the onset of fog at Brunei, based on a desktop computer and routine surface observations of dry bulb temperature (T), dewpoint temperature (T _d ), and wind speed at 1800 Local Time (LT). Optimism exists in improved predictions of fog and stratus as 1-D models incorporate many physical processes, and mesoscale models continue to improve in predicting advection and cloud cover.     

Parametric sensitivity analysis of precipitation and temperature based on multi-uncertainty quantification methods in the Weather Research and Forecasting model

Sensitivity analysis(SA) has been widely used to screen out a small number of sensitive parameters for model outputs from all adjustable parameters in weather and climate models, helping to improve model predictions by tuning the parameters. However, most parametric SA studies have focused on a single SA method and a single model output evaluation function, which makes the screened sensitive parameters less comprehensive. In addition, qualitative SA methods are often used because simulations using complex weather and climate models are time-consuming. Unlike previous SA studies, this research has systematically evaluated the sensitivity of parameters that affect precipitation and temperature simulations in the Weather Research and Forecasting(WRF) model using both qualitative and quantitative global SA methods. In the SA studies, multiple model output evaluation functions were used to conduct various SA experiments for precipitation and temperature. The results showed that five parameters(P3, P5, P7, P10, and P16) had the greatest effect on precipitation simulation results and that two parameters(P7 and P10) had the greatest effect for temperature. Using quantitative SA, the two-way interactive effect between P7 and P10 was also found to be important, especially for precipitation. The microphysics scheme had more sensitive parameters for precipitation, and P10(the multiplier for saturated soil water content) was the most sensitive parameter for both precipitation and temperature. From the ensemble simulations, preliminary results indicated that the precipitation and temperature simulation accuracies could be improved by tuning the respective sensitive parameter values, especially for simulations of moderate and heavy rain.     

Crustal Structure of the Korean Peninsula Using Surface Wave Dispersion and Numerical Modeling

Surface wave dispersion is studied to obtain the 1-D average velocity structure of the crust in the Korean Peninsula by inverting group- and phase-velocities jointly. Group velocities of short-period Rayleigh and Love waves are obtained from cross-correlations of seismic noise. Multiple-filter analysis is used to extract the group velocities at periods between 0.5 and 20 s. Phase velocities of Rayleigh waves in 10- and 50-s periods are obtained by applying the two-station method to teleseismic data. Dispersion curves of all group and phase velocities are jointly inverted for the 1-D average model of the Korean Peninsula. The resultant model from surface wave analysis can be used as an initial model for numerical modeling of observations of North Korean events for a velocity model appropriated to the Korean Peninsula. The iterative process is focused especially on the surface sedimentary layer in the numerical modeling. The final model, modified by numerical modeling from the initial model, indicates that the crust shear wave velocity increases with depth from 2.16 km/s for a 2-km-thick surface sedimentary layer to 3.79 km/s at a Moho depth of 33 km, and the upper mantle has a velocity of 4.70 km/s.     

On the Integrability of Limited-Area Numerical Weather Prediction Model ALADIN over Extended Time Periods

This note summarizes results of the first integration of regional numerical weather prediction model ALADIN in a climate mode. The ALADIN model, developed in an international cooperation led by Météo France, is operationally used for weather prediction. The grid step of the model is 12 km; the integration domain covers a major part of Europe. A one-month-long run has been performed with this model on observed boundary conditions (represented by assimilations by the global model ARPEGE). It is demonstrated that no excessive error is generated and accumulated in the model during the integration; hence the model is integrable for extended time periods and may serve a basis for a development towards a regional climate model.     

Sensitivity of the Weather Research and Forecasting (WRF) model to downscaling ratios and storm types in rainfall simulation

Accurate information of rainfall is needed for sustainable water management and more reliable flood forecasting. The advances in mesoscale numerical weather modelling and modern computing technologies make it possible to provide rainfall simulations and forecasts at increasingly higher resolutions in space and time. However, being one of the most difficult variables to be modelled, the quality of the rainfall products from the numerical weather model remains unsatisfactory for hydrological applications. In this study, the sensitivity of the Weather Research and Forecasting (WRF) model is investigated using different domain settings and various storm types to improve the model performance of rainfall simulation. Eight 24‐h storm events are selected from the Brue catchment, southwest England, with different spatial and temporal distributions of the rainfall intensity. Five domain configuration scenarios designed with gradually changing downscaling ratios are used to run the WRF model with the ECMWF 40‐year reanalysis data for the periods of the eight events. A two‐dimensional verification scheme is proposed to evaluate the amounts and distributions of simulated rainfall in both spatial and temporal dimensions. The verification scheme consists of both categorical and continuous indices for a first‐level assessment and a more quantitative evaluation of the simulated rainfall. The results reveal a general improvement of the model performance as we downscale from the outermost to the innermost domain. Moderate downscaling ratios of 1:7, 1:5 and 1:3 are found to perform better with the WRF model in giving more reasonable results than smaller ratios. For the sensitivity study on different storm types, the model shows the best performance in reproducing the storm events with spatial and temporal evenness of the observed rainfall, whereas the type of events with highly concentrated rainfall in space and time are found to be the trickiest case for WRF to handle. Finally, the efficiencies of several variability indices are verified in categorising the storm events on the basis of the two‐dimensional rainfall evenness, which could provide a more quantitative way for the event classification that facilitates further studies. It is important that similar studies with various storm events are carried out in other catchments with different geographic and climatic conditions, so that more general error patterns can be found and further improvements can be made to the rainfall products from mesoscale numerical weather models. Copyright © 2011 John Wiley & Sons, Ltd.     

Simulating a Highly Parameterized Groundwater Model for a Tropical Eogenetic Karst Aquifer Using Physically-Based Numerical Modeling and Inverse Method

Water Resources - Formulation of effective water management based on the understanding of the interaction between climate and sub-surface components as well as its causative consequences to...     

利用系统工程促进地震预报工作的快速发展

地震一直是影响人类生存的天灾,尤其是大的地震。30年前的唐山大地震记忆犹新,2008年的汶川特大地震更是震惊中外,其损失之大、牺牲之惨烈、可歌可泣的事迹让我们流泪、让我们感动、终身难忘。痛定思痛,我们一定要切实加强地震预报工作。由于地震预报工作涉及的学科很多,涉及的面又相当广,例如,这次电磁会议上所报告的,在地震前是有一些预兆的,它是个复杂系统,需要有系统的思想,并可以利用系统工程。