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Visible and infrared photos obtained by the Mirage 640 were taken during the May 2022 SAIL TBS deployment. Imaging purposes include trying to track the sensible heat flux and surface variabilty in the snowpack.
Models used to study the changes in Martian climate must of necessity adequately describe the current volatile cycles. The focus is on mathematical modeling of current volatile cycles and modest extrapolation to past regimes. Water cycle, dust storms, polar variabilty, and north polar clouds data were compared to the model predictions.
Measurements from the Retarding Ion Mass Spectrometer on the Dynamics Explorer have, for the first time, revealed a supersonic polar wind along polar cap field lines. The observations reported were obtained on the nightside (22:30 to 23:30 MLT) from 65 to 81 deg invariant latitude and at altitudes near 2 earth radii. Fitting the data using a thin-sheath model gives a range of temperatures of 0.1 to 0.2 eV with corresponding flow velocities of 25 to 16 km/s over the estimated range of spacecraft potential of +3 to +5 V. For these values the Mach number ranged from 5.1 to 2.6 (with a most likely value of 3). Characteristics of the H(+) flow are in general agreement with those predicted by 'classical' polar wind theory, but high variabilty of the He(+)/H(+) ratio was observed.
A data base comprised of all available ionosphere satellite ion composition measurements - the Goddard Comprehensive Ionosphere Data Base (GCID) - has been set upon optical disks for convenient merging and accessing of data from different satellites. This data has recentely been expanded to include all accessible satellite electron density and plasma temperature measurements. This paper demonstrates with a couple examples, the potential of GCID as an alternative to empirical models for undertanding ionsophere physics and chemistry. Through binning techniques GCID provdes a tool that complements and improves on the International Reference Ionosphere model (IRI) in delineating the topology of high latitude ion composition. GCID provides at a glance a measure of the local statistical variabilty of the ion compostions that is not in empirical models such as IRI. Furthermusing the data to determine the spatial and geophysical parameter range over which the minor ion species are approximately in chemical equilibrium, the number of data points available for empirical ion composition models can be increased by using the statiscally more reliable neutral and electron empirical models to derive ions composition. Currently available empirical models are not capable of adequately defining the comples high latitude distribution - a measure of the variablity is needed.
In the spring of 1992 an optical closure experiment was conducted at Lake Pend Orielle, Idaho. A primary objective of the experiment was to compare techniques for the measurement of the spectral absorption coefficent and other inherent optical properties of natural waters. Daily averages of absorption coefficents measured using six methods are compared at wavelengths of 456, 488, and 532 nm. Overall agreement was within 40% at 456 nm and improved with increasing wavelength to 25% at 532 nm. These absorption measurements were distributed over the final 9 days of the experiement, when bio-optical conditions in Lake Pend Oreille (as indexed by the beam attenuation coefficent c(sub p)(660) and chlorophyll a fluorescence profiles) were representative of those observed throughout the experiment. However, profiles of stimulated chlorophyll a fluorescence and beam transmission showed that bio-optical properties in the lake varied strongly on all time and space scales. Therefore environmental variabilty contributed significantly to deviations between daily mean absorption coefficients measured using the different techniques.
Direct estimates of surface radiative fluxes that resolve regional and weather-scale variabilty over the whole globe with reasonable accuracy have only become possible with the advent of extensive global, mostly satellite, datasets within the past couple of decades. The accuracy of these fluxes, estimated to be about 10-15 W per square meter is largely limited by the accuracy of the input datasets. The leading uncertainties in the surface fluxes are no longer predominantly induced by clouds but are now as much associated with uncertainties in the surface and near-surface atmospheric properties. This study presents a fuller, more quantitative evaluation of the uncertainties for the surface albedo and emissivity and surface skin temperatures by comparing the main available global datasets from the Moderate-Resolution Imaging Spectroradiometer product, the NASA Global Energy and Water Cycle Experiment Surface Radiation Budget project, the European Centre for Medium-Range Weather Forecasts, the National Aeronautics and Space Administration, the National Centers for Environmental Prediction, the International Satellite Cloud Climatology Project (ISCCP), the Laboratoire de Meteorologie Dynamique, NOAA/NASA Pathfinder Advanced Very High Resolution Radiometer project, NOAA Optimum Interpolation Sea Surface Temperature Analysis and the Tropical Rainfall Measuring Mission (TRMM) Microwave Image project. The datasets are, in practice, treated as an ensemble of realizations of the actual climate such that their differences represent an estimate of the uncertainty in their measurements because we do not possess global truth datasets for these quantities. The results are globally representative and may be taken as a generalization of our previous ISCCP-based uncertainty estimates for the input datasets. Surface properties have the primary role in determining the surface upward shortwave (SW) and longwave (LW) flux. From this study, the following conclusions are obtained. Although land surface albedos in the near near-infrared remain poorly constrained (highly uncertain), they do not cause too much error in total surface SW fluxes; the more subtle regional and seasonal variations associated with vegetation and snow are still on doubt. The uncertainty of the broadband black-sky SW albedo for land surface from this study is about 7%, which can easily induce 5-10 W per square meter uncertainty in (upwelling) surface SW flux estimates. Even though available surface (broadband) LW emissivity datasets differ significantly (3%-5% uncertainty), this disagreement is confined to wavelengths greater than 20 micrometers so that there is little practical effect (1-3 W per square meters) on the surface upwelling LW fluxes. The surface skin temperature is one of two leading factors that cause problems with surface LW fluxes. Even though the differences among the various datasets are generally only 2-4 K, this can easily cause 10-15 W per square meter uncertainty in calculated surface (upwelling) LW fluxes. Significant improvements could be obtained for surface LW flux calculations by improving the retrievals of (in order of decreasing importance): (1) surface skin temperature, (2) surface air and near-surface-layer temperature, (3) column precipitable water amount and (4) broadband emissivity. And for surface SW fluxes, improvements could be obtained (excluding improved cloud treatment) by improving the retrievals of (1) aerosols (from our sensitivity studies but not discussed in this work), and (2) surface (black-sky) albedo, of which, NIR part of the spectrum has much larger uncertainty.
An assessment of simulations of the interannual variability of tropical cyclones (TCs) over the western North Pacific (WNP) and its association with El Niño-Southern Oscillation (ENSO), as well as a subsequent diagnosis for possible causes of model biases generated from simulated large-scale climate conditions, are documented in the paper. The model experiments are carried out by the Hurricane Work Group under the U.S. Climate Variability and Predictability Research Program (CLIVAR) using five global climate models (GCMs) with a total of 16 ensemble members forced by the observed sea surface temperature and spanning the 28-yr period from 1982 to 2009. The results show GISS and GFDL model ensemble means best simulate the interannual variability of TCs, and the multimodel ensemble mean (MME) follows. Also, the MME has the closest climate mean annual number of WNP TCs and the smallest root-mean-square error to the observation. Most GCMs can simulate the interannual variability of WNP TCs well, with stronger TC activities during two types of El Niño-namely, eastern Pacific (EP) and central Pacific (CP) El Niño-and weaker activity during La Niña. However, none of the models capture the differences in TC activity between EP and CP El Niño as are shown in observations. The inability of models to distinguish the differences in TC activities between the two types of El Niño events may be due to the bias of the models in response to the shift of tropical heating associated with CP El Niño.
This presentation provides an overview of wind forecasting, flexibility, uncertainty, variability, and integration.