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An overview of two years of data obtained with a ground-based microwave instrument is given. Intercomparisons with data obtained by the co-located JPL lidar and by SAGE 2 during near overpasses of the site are discussed, as are comparisons with mesospheric data taken earlier by SME and LIMS. Observations of diurnal variations of mesospheric ozone are shown.
Severe storms research is characterized by temporal scales ranging from minutes (for thunderstorms and tornadoes) to hours (for hurricanes and extra-tropical cyclones). Spatial scales range from tens to hundreds of kilometers. Sources of observational data include a variety of ground based and satellite systems. Requirements for registration and intercomparison of data from these various sources are examined and the potential for operational forecasting application of techniques resulting from the research is discussed. The sensor characteristics and processing procedures relating to the overlay and integrated analysis of satellite and surface observations for severe storms research are reviewed.
The Coupled Model Intercomparison Project (CMIP) is a flagship of the World Climate Research Programme (WCRP). CMIP has become a recognised ‘brand’ in climate circles evolving over the last thirty years from a targeted research activity by a small number of climate modelling centres intercomparing their Earth System Model (ESM) simulations to a broad international coordinated research effort (Durack et al, 2025). CMIP is organized as a research activity leveraging funded and in-kind contributions from experts within modelling centres and the broader scientific community supported more recently by a fully-funded International Project Office. Within CMIP, Model Intercomparison Projects (MIPs) are community-designed to understand past, present and future climate. CMIP data provides a valuable resource for climate research and is routinely used to assess model representation of climate processes and test scientific hypotheses in the context of model uncertainty and (forced and internal) variability as evident from its prolific use in scientific publications1 . The impact relies on enabling infrastructure (most prominently via the Earth System Grid Federation (ESGF)), which allows sharing of simulation output, provision of the boundary conditions used in each simulation, and definition of the data standards that are essential to facilitating wide use of the data. The impact is supplemented by the wide-ranging scrutiny to which model simulations are subjected. Beyond its use in research, CMIP data is a key resource for communities producing derived climate information from downscaling and impact studies, such as the Coordinated Regional Downscaling Experiment (CORDEX; Gutowski et al., 2016) and the Intersectoral Impacts MIP (ISIMIP; Frieler et al., 2024). Government, academic and commercial entities also increasingly rely on CMIP and its downstream data for climate risk assessments and climate services (for example, Copernicus Climate Change Service and World Bank portal). This means that, although CMIP is a research activity, it increasingly serves a secondary and very relevant role as a provider of climate data – a long-recognised dichotomy (Stevens, 2024). Research and applications have distinct needs, with the former requiring flexibility and generality and the latter consistency. Here we explain how the design of the research activity has been adapted to reduce the burdens imposed by applications and how the research infrastructure might evolve to further enable scientific inquiry. We propose one possible approach to consistently providing model information and projections for applications in the future.
This lecture describes a technique by which atmospheric measurements of trace species with medium to long lifetimes can be 'coincidentally' compared and validated even though measurements are taken at different locations and different times In other words, the method, under suitable counditions, can remove a large amount of the natural meteorological variability. The technique involves the use of quasi-Lagrangian or conservative coordinates - air parcel tags which are invariant or nearly invariant under the motion of the parcel. The technique is called 'reconstruction', since measurements taken at one location can be reconstructed at different locations. In order to do this, the data are transformed into the conservative coordinates and accumulated. Within the conservative reference frame, much of the meteorological variability is removed. Once enough data are obtained within the system, the observations can be tranformed back into physical space at any location and compared with other measurements. The method by which the trace species data are obtained makes no difference; satellite, balloon, rocket, aircraft and ground-based data all become equivalent and can be intercompared. The conservative-coordinate system not only allows for intercomparison of data, but shows how data can be taken in such a way as to maximize the physical scope of the information. In other words, the method automatically suggests when conditions might be suitable to obtain information with different environmental situations. It also allows for the forecast of constituent fields using only the meteorological forecasts and limited observational data.
The Nimbus 7 Total Ozone Mapping Spectrometer (TOMS) data over Europe have been processed for 2 days of the ozone sensor intercomparison balloon campaign in June 1981 at Gap, France. The June 19 TOMS map shows an elongated ozone maximum (about 400 D.U) extending in a NW-SE direction across central Europe and an ozone minimum (300 D.U.) in the central Atlantic. This pattern produces a 12 D.U. decrease along the balloon trajectory from the fist ascent to the second descent. On June 26 a much smaller ozone maximum (400 D.U.) is centered in northern France with its main axis running in a SW-NE direction. The total ozone contours tend to lie parallel to the balloon trajectory so that the total ozone during the flight is nearly constant.
New online prototypes have been developed to extend and enhance the previous effort by facilitating investigation of product characteristics and intercomparison of precipitation products in different algorithms as well as in different versions at different spatial scales ranging from local to global without downloading data and software. Several popular Tropical Rainfall Measuring Mission (TRMM) products and the TRMM Composite Climatology are included. In addition, users can download customized data in several popular formats for further analysis. Examples show product quality problems and differences in several monthly precipitation products. It is seen that differences in daily and monthly precipitation products are distributed unevenly in space and it is necessary to have tools such as those presented here for customized and detailed investigations. A simple time series and two area maps allow the discovery of abnormal values of 3A25 in one of the months. An example shows a V-shaped valley issue in the Version 6 3B43 time series and another example shows a sudden drop in 3A25 monthly rain rate, all of which provide important information when the products are used for long-term trend studies. Future plans include adding more products and statistical functionality in the prototypes.
Validation of satellite remote-sensing methods for estimating rainfall against rain-gauge data is attractive because of the direct nature of the rain-gauge measurements. Comparisons of satellite estimates to rain-gauge data are difficult, however, because of the extreme variability of rain and the fact that satellites view large areas over a short time while rain gauges monitor small areas continuously. In this paper, a statistical model of rainfall variability developed for studies of sampling error in averages of satellite data is used to examine the impact of spatial and temporal averaging of satellite and gauge data on intercomparison results. The model parameters were derived from radar observations of rain, but the model appears to capture many of the characteristics of rain-gauge data as well. The model predicts that many months of data from areas containing a few gauges are required to validate satellite estimates over the areas, and that the areas should be of the order of several hundred km in diameter. Over gauge arrays of sufficiently high density, the optimal areas and averaging times are reduced. The possibility of using time-weighted averages of gauge data is explored.
Results are presented of an intercomparison between MSU channel 2 Tb and radiosonde data taking both oxygen and water vapor absorption into account. A total of 45 stations covering most of the central and eastern U.S. were included. Precise comparisons (to 0.1 C or better) between MSU Tb and radiosonde-calculated Tb were found to be possible with large numbers of stations and long averaging periods (many months), although they are not sufficient in number to address the previously documented monthly precision of 0.01 C from satellite-satellite intercomparisons. The MSU channel 2 does not have a 1:1 response to atmospheric temperature variations; the response averages about 92 percent. No evidence was found to suggest that procedural or hardware changes in the U.S. radiosonde system have caused any widespread biases between 1980 and 1986.
The validity of ozone-profile data from the satellite sensor SAGE was tested in a series of correlative experiments conducted at five fixed sites between 6 deg S and 65 deg N during 1979-1980. The intercomparisons included data taken with electrochemical ozone (ECC) balloonsondes and chemiluminescent and optical rocketsondes. The average mean difference for 17 separate comparisons between the SAGE and ECC balloonsonde observations over the altitudes 18-28 km was 9.3 percent with a standard deviation of 2.8 percent. Excluding comparisons separated by greater than 500 km reduces the average mean difference to 8.9 percent and the standard deviation to 2.1 percent. The average mean difference between SAGE and three optical rocketsonde observations over the altitudes 25-50 km was 11 percent, and between SAGE and two chemiluminescent rocketsondes over the altitudes 20-60 km it was 13.5 percent. Considering the differences in vertical resolution, experimental errors, and ozone time and space gradients, the agreement between SAGE-derived ozone profiles and these correlative measurements is considered very good. In addition, isopleths of ozone mixing ratio versus latitude and altitude are in good agreement with previously published results.
A Python-based metrics and diagnostics package is currently being developed by the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Infrastructure Team at Lawrence Livermore National Laboratory (LLNL) to facilitate the use of long-term, high-frequency measurements from the ARM Facility in evaluating the regional climate simulation of clouds, radiation, and precipitation. This metrics and diagnostics package computes climatological means of targeted climate model simulation and generates tables and plots for comparing the model simulation with ARM observational data. The Coupled Model Intercomparison Project (CMIP) model data sets are also included in the package to enable model intercomparison as demonstrated in Zhang et al. (2017). The mean of the CMIP model can serve as a reference for individual models. Basic performance metrics are computed to measure the accuracy of mean state and variability of climate models. The evaluated physical quantities include cloud fraction, temperature, relative humidity, cloud liquid water path, total column water vapor, precipitation, sensible and latent heat fluxes, and radiative fluxes, with plan to extend to more fields, such as aerosol and microphysics properties. Process-oriented diagnostics focusing on individual cloud- and precipitation-related phenomena are also being developed for the evaluation and development of specific model physical parameterizations. The version 1.0 package is designed based on data collected at ARM’s Southern Great Plains (SGP) Research Facility, with the plan to extend to other ARM sites. The metrics and diagnostics package is currently built upon standard Python libraries and additional Python packages developed by DOE (such as CDMS and CDAT). The ARM metrics and diagnostic package is available publicly with the hope that it can serve as an easy entry point for climate modelers to compare their models with ARM data. In this report, we first present the input data, which constitutes the core content of the metrics and diagnostics package in section 2, and a user's guide documenting the workflow/structure of the version 1.0 codes, and including step-by-step instruction for running the package in section 3.
This grant funded observations, data reduction, professional publications and travel for scientific efforts on the Kuiper Airborne Observatory. The research project was successfully completed. New insights into the distribution of far infrared emission across star forming regions was obtained, and student training was achieved. The efforts contributed towards new observing strategies, such as calibration and intercomparison of data from different infrared astronomical observing platforms, that will impact future NASA missions, such as SOFIA. The results of the effort have been presented in several papers in the refereed literature, including: "The Structure of IR Luminous Galaxies at 100 Microns". " Far Infrared Thermal Emission from the Inner Cooling Flow Region of NGC1275". "Distribution of Light in the "Dusty Hand" Galaxy NGC2146".
During the course of the DYnamics Adapted Network for the Atmosphere (DYANA) campaign in early 1990, various techniques to measure densities and temperatures from the ground up to the lower thermosphere were employed. Some of these measurements were performed near simultaneously (maximum allowed time difference: 1 h) and at the same location, and therefore offered the unique chance of intercomparison of different techniques. In this study, we will report on intercomparisons of data from ground-based instruments (Rayleigh- and sodium-lidar), balloon-borne methods (datasondes and radiosondes) and rocket-borne techniques (falling spheres and ionization gauges). The main result is that there is good agreement between the various measurements when considering the error bars. Only occasionally did we notice small but systematic differences (e.g. for the datasondes above 65 km). The most extensive intercomparison was possible between the Rayleigh lidar and the falling sphere technique, both employed in Biscarrosse (44 deg N, 1 deg W). Concerning densities, excellent agreement was found below 63 km: the mean of the deviations is less than 1% and the root mean square (RMS) is approximately 3%. Systematic differences of the order of 5% were noticed around 67 km and above 80 km. The former can be accounted for by an instrumental effect of the falling sphere (Ma = 1 transition; Ma = Mach number), whereas the latter is tentatively explained by the presence of Mie scatterers in the upper mesosphere. Concerning temperatures, the agreement is excellent between 35 and 65 km: the mean of the deviations is less than +/- 3 K and the variability is +/- 5 K. The two systematic density differences mentioned above also affect the temperatures: between 65 and 80 km, the Rayleigh lidar temperatures are systematically lower than the falling sphere values by approximately 5 K.
The Sensor Intercomparison and Merger for Biological and Interdisciplinary Oceanic Studies (SIMBIOS) Project is concerned with ocean color satellite sensor data intercomparison and merger for biological and interdisciplinary studies of the global oceans. Imagery from different ocean color sensors can now be processed by a single software package using the same algorithms, adjusted by different sensor spectral characteristics, and the same ancillary meteorological and environmental data. This enables cross-comparison and validation of the data derived from satellite sensors and, consequently, creates continuity in ocean color information on both the temporal and spatial scale. The next step in this process is the integration of in situ ocean and atmospheric parameters to enable cross-validation and further refinement of the ocean color methodology. The SIMBIOS Project Office accomplishments during 2000 year are summarized under satellite data processing, data product validation, SeaWiFS Bio-Optical Archive and Storage System (SeaBASS) database, supporting services, sun photometers and calibration activities, and calibration round robins. These accomplishments are described.
Recurrent low energy (greater than or = to 0.5 MeV) proton flux enhancements, reliable indicators of corotating plasma interaction regions, were observed on the Voyager 1 and 2 and Pioneer 11 spacecraft in the heliographic latitude range 2 deg S to 23 N and the heliocentric radial range 11 to 20 AU. After a period of rather high correlation between fluxes at different latitudes in early 1983, distinct differences developed in the fluxes during an overall flux decrease. The flux intensities returned to higher levels in early 1984 and differences in both the recurrence frequency and flux intensity persisted into 1985, as Voyager 1 traveled to 23 AU and 25 N latitude. Intercomparison of data from the three spacecraft indicates that the flux differences are most likely due to latitudinal rather than radial or temporal variations.
Active and passive microwave and physical properties of Arctic sea ice in the marginal ice zone were measured during the summer. Results of an intercomparison of data acquired by an aircraft synthetic aperture radar, a passive microwave imager and a helicopter-mounted scatterometer indicate that early-to-mid summer sea ice microwave signatures are dominated by snowpack characteristics. Measurements show that the greatest contrast between thin first-year and multiyear sea ice occurs when operating actively between 5 and 10 GHz. Significant information about the state of melt of snow and ice is contained in the active and passive microwave signatures.
Earth Radiation Budget Experiment solar total irradiance data obtained by the solar monitors of the NOAA 9 and 10 spacecraft are presently compared with those obtained by the Solar Maximum Mission's Active Cavity Irradiance Monitor 1, as well as sounding rockets and the Nimbus 7 Earth Radiation Budget channel 10C. A disparity is noted among total irradiance values obtained in near-simultaneous measurements with absolute pyrheliometers of similar design and operational modes which (1) indicates the uncertainty associated with currently available instruments, and (2) recommends intercalibration of electrically self-calibrating pyrheliometers from spacecraft.
At the end of 1992, the Pioneer 10, Pioneer 11, and Voyager 2 spacecraft were at heliocentric distances of 56.0, 37.3, and 39.0 AU and heliographic latitudes of 3.3 deg N, 17.4 deg N, and 8.6 deg S, respectively. Pioneer 11 and Voyager 2 are at similar celestial longitudes, while Pioneer 10 is on the opposite side of the Sun. All three spacecraft have working plasma analyzers, so intercomparison of data from these spacecraft provides important information about the global character of the solar wind in the outer heliosphere. The averaged solar wind speed continued to exhibit its well-known variation with solar cycle: Even at heliocentric distances greater than 50 AU, the average speed is highest during the declining phase of the solar cycle and lowest near solar minimum. There was a strong latitudinal gradient in solar wind speed between 3 deg and 17 deg N during the last solar minimum, but this gradient has since disappeared. The solar wind temperature declined with increasing heliocentric distance out to a heliocentric distance of at least 20 AU; this decline appeared to continue at larger heliocentric distances, but temperatures in the outer heliosphere were suprisingly high. While Pioneer 10 and Voyager 2 observed comparable solar wind temperatures, the temperature at Pioneer 11 was significantly higher, which suggests the existence of a large-scale variation of temperature with heliographic longitude. There was also some suggestion that solar wind temperatures were higher near solar minimum.
A unique complement of passive and active microwave imaging and sensing instruments for observing ocean surface emission and scattering signatures were integrated onto the NASA Wallops Flight Facility's Orion P-3B aircraft (N426NA) for the purpose of studying the signature of ocean surface winds. The complement included: (1) a, four-band (X, K, Ka, and W) tri-polarimetric scanning radiometer (PSR), (2) a C-band ocean surface scatterometer (CSCAT), (3) a Ka-band conical-scanning polarimetric radiometer (KASPR), (4) a nadir-viewing Ka-band polarimetric radiometer, (KAPOL), (5) a 21- and 31-GHz zenith-viewing cloud and water vapor radiometer (CWVR), and (6) a radar ocean wave spectrometer (ROWS). The above Ocean Winds Imaging (OWI) complement was flown during January-March, 1997 over the Labrador Sea. Conically-scanned brightness temperature and backscatter imagery were observed over open ocean for a variety of wind speeds and cloud conditions. Presented herein are the results of a preliminary intercomparison of data from several of the OWI instruments.