Historical Atmospheric Data Research Articles

Prediction and observation of regional infrasound from the OSIRIS REx re-entry capsule

Infrasonic signals produced by bolides and space debris passing through atmosphere are frequently observed at notable stand-off distances. The acoustic energy radiated from such objects is dominated by the aero-acoustic Mach cone which exhibits a highly directional radiation pattern dependent on the velocity of the object relative to the surrounding atmosphere. Recently, a mapping of the Mach cone geometry into acoustic ray tracing methods has enabled efficient regional simulation of infrasonic ensonification from such sources. In September 2023, the OSIRIS REx sample capsule returned to Earth as part of a NASA supported asteroid study and a number of institutions deployed sensors to measure geophysical signatures of the event. The anticipated capsule trajectory was combined with historical atmospheric data for September in the western United States to predict likely regional ensonification during re-entry. Los Alamos National Laboratory infrasound experts deployed microbarometer arrays in locations predicted to be ensonified and were able to identify signals with arrival times and direction-of-arrival consistent with the capsule re-entry. An overview of the predictions, field deployments, and identified signals will be presented.

Atmospheric Variability and Prevalence of Common Psychiatric Disorders in South Asia: A Meta-regressive Analysis

Background: The review of literature suggests that there is a dearth of meta-analytical study that examines the role of Atmospheric Variability on the prevalence of mental disorders in South Asia. Aims &Objectives: Therefore, the present study explores the moderating role of variability in temperature, air pressure, humidity, and rainfall on the prevalence of Common Psychiatric Disorders in South Asia. Material & Methods: Databases of several web sources, namely, EBSCOhost, PubMed, PsycINFO, and Google Scholar were explored for the studies that had previously observed the prevalence of psychiatric morbidity in South Asian countries. Further, articles were also examined manually. Initially, geographical locations (i.e. latitude, longitude, and altitude) of surveyed places were determined. Based on these locations, historical atmospheric data were retrieved. Meta-regression analysis was computed using R –software with ‘metafor’ package. Results: The present Meta-analysis included 32 epidemiological studies consisting of 110402 persons reported a total morbidity in 7935 persons across seven countries of South Asia. Yearly rainfall (z=2.8260, p<0.01), yearly variability in temperature (z=3.7160, p<0.001), yearly variability in humidity (z= -2.4031, p<0.05) appear to have a significant influence on the prevailing patterns of common psychiatric disorders. However, yearly variability in atmospheric pressure did not have a significant influence on the prevalence of mental disorders (z= 1.0364, p>0.300). Conclusion: Discomfort weather conditions such as yearly temperature variability, excessive rainfall, and yearly variability in humidity have a significant role in the occurrence and maintenance of different psychiatric disorders in South Asia.

Special Issue on Modeling of Hydroclimate and Climate Change

The Earth is fundamentally a system of dynamically interacting oceanic, atmospheric, and land components. As such, utilization of coupled models has significant advantages over the models that focus on each of the components separately and use information from other components as their boundary conditions or forcing functions/inputs. When the atmospheric and land surface hydrologic processes are modeled as a coupled system, the atmospheric inputs for the land hydrologic component, such as precipitation, radiation, and relative humidity, are automatically supplied to the hydrologic component as a result of the solution of the coupled system versus needing such atmospheric information as external input to a standalone hydrologic component model. This creates a very significant advantage in solving such outstanding problems as that of ungauged basins. When performing coupled atmospherichydrologic modeling over such basins, the unknown precipitation and other atmospheric inputs for the hydrologic component are solved as part of the coupled system, providing the crucial information for the solution of the hydrology of the ungauged basin. Similarly, linking oceanic processes with hydrologic processes or atmospheric processes may also transfer some crucial information, such as sea surface temperatures, for the effective long-term prediction of hydrologic processes in watersheds that may be far removed from the ocean, which may be the primary source of moisture supply to the particular watershed. Starting in the late 1980s and early 1990s, hydrologic engineers began to realize the value of coupled interactive modeling of hydrologic processes together with atmospheric or oceanic processes and started developing physically based regional-scale (mesoscale) coupled atmospheric-hydrologic models. Such models are called hydroclimate models. Similarly, hydrologic engineers began to utilize various statistical tools in trying to link various oceanic process indexes with the hydrologic variables to improve hydrologic forecasting. This special issue of the Journal of Hydrologic Engineering on the modeling of hydroclimate and climate change focuses on such numerical coupled atmospheric-hydrologic hydroclimate models and various statistical approaches toward modeling various hydrologic and climatic processes over large watersheds and regions for the long-term forecasting of hydrologic processes, for water resources planning, and for assessing the effect of climate change on the hydrologic regime of a region. Within the previously discussed framework, the papers in this special issue are separated into two primary categories: (1) statistical approaches toward linking various components of the earth system toward improving long-term hydrologic forecasting and (2) numerical regional-scale coupled atmospheric-hydrologic hydroclimate modeling approaches for the reconstruction of nonexistent historical hydroclimate data for utilization in water resources planning and management studies and for dynamical downscaling of global-scale coarse resolution historical climate data and future GCM climate projection data onto regions of interest at fine space-time resolution to assess the effect of climate change on the hydrologic regime and water resources of the particular regions. The papers titled “Appraisal of Statistical Predictability under Uncertain Inputs: SST to Rainfall,” “Association between Uncertainties in Meteorological Variables and Water Resources Planning for the State of Texas,” “Basis for Extending Long-Term Streamflow Forecasts in the Colorado River Basin,” and “Scaling Characteristics of Precipitation Data over Texas” fall into the category of statistical approaches. Meanwhile, the papers titled “Application for a Coupled Regional-Scale HydrologicalAtmospheric Model to Japan for Climate Change Study,” “Regional Modeling of Climate Change Impact on Peninsular Malaysia Water Resources,” “Reconstruction of Historical Atmospheric Data by a Hydroclimate Model for the Mekong River Basin,” “Upscaling of Coupled Land Surface Process Modeling for Heterogeneous Landscapes: Stochastic Approach,” “Coupled Regional Hydroclimate Model and Its Application to the TigrisEuphrates Basin,” “AWater Balance Study for the Tigris-Euphrates River Basin,” and “Impact of Water Resources Utilization on the Hydrology of Mesopotamian Marshlands” fall into the category of numerical regional-scale coupled atmospheric-hydrologic hydroclimate modeling approaches. It is the hope of this guest editor that this special issue shall become the first in a sequence of special issues on the topic of modeling hydroclimate and climate change as the field advances and matures in the future.

Modelling Extreme Storm-Induced Currents over the Grand Banks

Extreme storm-induced currents over the Grand Banks are investigated using a three-dimensional (3-D) ocean circulation model forced by 22 storms selected from the past 50 years with return intervals ranging from 1 to 34 years. Wind data for the storms are historical atmospheric data. The modelled currents are compared with current meter measurements made during a storm in March 1983. The results indicate good agreement between the model and measurements. In the surface layer of the Grand Banks, the model extreme current speeds are approximately 80 cm s−1 over a large portion of the Grand Banks, and some areas have extreme current speeds higher than 120 cm s−1. The highest extreme current speeds occur at St. Pierre Bank, where the speed reaches 140 cm s−1. In the bottom layer, the region with high extreme current speeds is mainly in the periphery of the Grand Banks, with magnitudes over 40 cm s−1. The results also show that the response of the water to storm forcing in the Grand Banks area varies from place to place because the mechanisms of current generation are different at different locations.

Reconstruction of Historical Atmospheric Data by a Hydroclimate Model for the Mekong River Basin

It is necessary to have historical atmospheric data at fine time and spatial grid resolutions as input to watershed-scale models of the Mekong River Basin to perform water balance studies over the basin. However, such historical atmospheric data are not available at the desired fine time-space scales for various reasons in this region. Therefore, it is necessary to reconstruct the atmospheric data over the Mekong River Basin at a fine spatial resolution (approximately 20 km) by means of a regional coupled atmospheric-hydrologic model from the available historical global atmospheric data that have a very coarse resolution (approximately 285 km). The coarse resolution (approximately 285 km) reanalysis of atmospheric data for 1956–2006 from the U.S. National Center for Atmospheric Research (NCAR) and the U.S. National Center for Environmental Prediction (NCEP) were used for the reconstruction of the fine resolution (approximately 20 km) historical atmospheric data over the basin. To incorporate the complex topographical and land surface features of this basin, a regional, fully coupled atmospheric-hydrologic hydro-climate model of the Mekong River Basin, called “RegHCM-Mekong,” was developed for the dynamic downscaling. The coarse time-space resolution NCEP-NCAR historical atmospheric data were then downscaled by RegHCM-Mekong to produce meteorological information on rainfall, air temperature, specific humidity, wind velocity, solar radiation, and net radiation at 0.2 degrees spatial resolution (approximately 20 km) and at hourly intervals over the whole Mekong River Basin during a historical period. After a bias correction of the RegHCM-Mekong-downscaled data (by means of Global Precipitation Climatology Project (GPCP) data), the RegHCM-Mekong-reconstructed historical rainfall fields were successfully validated through comparisons against available point-location observations. The comparisons against the interpolated spatial maps of historical precipitation showed satisfactory results.

Stochastic Six-Degree-of-Freedom Flight Simulator for Passively Controlled High-Power Rockets

This paper presents a method for simulating the flight of a passively controlled rocket in six degrees of freedom, and the descent under parachute in three degrees of freedom. Also presented is a method for modeling the uncertainty in both the rocket dynamics and the atmospheric conditions using stochastic parameters and the Monte Carlo method. Included within this, we present a method for quantifying the uncertainty in the atmospheric conditions using historical atmospheric data. The core simulation algorithm is a numerical integration of the rocket’s equations of motion using the Runge-Kutta-Fehlberg method. The position of the rocket’s center of mass is described using three dimensional Cartesian coordinates and the rocket’s orientation is described using quaternions. Input parameters to the simulator are made stochastic by adding Gaussian noise. In the case of atmospheric parameters, the variance of the noise is a function of altitude and noise at adjacent altitudes is correlated. The core simulation ...

Reconstructed Historical Atmospheric Data by Dynamical Downscaling

A 43-year database of reconstructed atmospheric data at 3-km spatial resolution and hourly temporal resolution was created using a nonhydrostatic regional atmospheric model and archived global, coarse-resolution atmospheric data. The archived atmospheric data at approximately 200-km spatial resolution and 6-h temporal resolution were used as initial and boundary conditions in the regional model to generate the reconstructed data. Data for seven variables were extracted from model results for the database: precipitation, atmospheric temperature, relative humidity, wind speed, incoming solar radiation, downward longwave radiation, and latent heat flux. In addition, dew point temperature was computed from simulation results and stored in the database. Model results for temperature and precipitation were compared to observed data collected at three sites in the basin over a 6-year period. Model-generated results match the seasonal, annual, and interannual fluctuations in the observed data quite well. Results indicate that the method for reconstructing atmospheric data for use in distributed parameter hydrologic and environmental models provides spatial detail unmatched by existing observed data networks and is a viable alternative to statistical manipulation of limited observed data.

A model for simulation of the climate and hydrology of the Great Lakes basin

The Coupled Hydrosphere‐Atmosphere Research Model (CHARM) was developed by coupling the Regional Atmospheric Modeling System (RAMS) to models of the land hydrology of the Great Lakes basin and of the evaporation and thermodynamics of the Great Lakes. It is intended for running coupled atmosphere‐surface climate scenarios for the Great Lakes basin, to gain a perspective that has been missed by running hydrologic models in off‐line mode, driven by the output of global general circulation models. This paper presents validation of this model using historical atmospheric data to drive the regional embedded CHARM model. The current version of CHARM simulates the near‐surface air temperature in the region quite well, with some positive bias during the winter and negative bias during the summer. Biases in the temperature averaged over 1 month and over the portion of the domain that is not directly forced by observations are less than or approximately 2 K. The annual precipitation has a positive bias of 6.6% and does well at placing the lake‐effect precipitation areas, but may have too strong a west‐east gradient. Simulation of annually averaged runoff meets well with expectations, but additional empirical fitting may be required to replicate the seasonal cycle. Aspects of the model that remain troublesome are the tendency for unrealistically low pressure at mean sea level and for persistent heavy low stratus clouds.

Searching for analogues, how long must we wait?

A three-way relationship is derived between the size of a library (M years) of historical atmospheric data, the distance between an arbitrarily picked state of the atmosphere and its nearest neighbor (or analogue), and the size of the spatial domain, as measured by the number of spatial degrees of freedom (N). It is found that it would take a library of order 1030 years to find 2 observed flows that match to within current observational error over a large area such as the Northern Hemisphere. Obviously, with only 10—100 years of data, the probability of finding natural analogous is very small, unless one is satisfied with analogy over small areas or in just 2 of 3 degrees of freedom as represented, for instance, by 2 or 3 leading empirical orthogonal modes. We further propose the notion that analogues can be constructed by combining a number of observed flow patterns. We have found at least one application where linearly constructed analogues are conclusively better at specifying US surface weather from concurrent 700 mb geopotential height than natural analogues are.