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AVHRR data record is well alive and continue to improve and be used by a large land user community.Most of the improvement are due to the overlapping with MODIS Aqua, Terra.We recommend operating missions as long as possible to enable overlap of at least a few years (especially for applications).
Clouds are of high importance for the climate system but they still remain one of its principal uncertainties. Remote sensing techniques applied to satellite observations have assisted tremendously in the creation of long-term and homogeneous data records; however, satellite data sets need to be validated and compared with other data records, especially ground measurements. In the present study, the spatiotemporal distribution and variability of Total Cloud Cover (TCC) from the Satellite Application Facility on Climate Monitoring (CM SAF) Cloud, Albedo And Surface Radiation dataset from AVHRR data—edition 2 (CLARA-A2) and the International Satellite Cloud Climatology Project H-series (ISCCP-H) is analyzed over Europe. The CLARA-A2 data record has been created using measurements of the Advanced Very High Resolution Radiometer (AVHRR) instrument onboard the polar orbiting NOAA and the EUMETSAT MetOp satellites, whereas the ISCCP-H data were produced by a combination of measurements from geostationary meteorological satellites and the AVHRR instrument on the polar orbiting satellites. An intercomparison of the two data records is performed over their common period, 1984 to 2012. In addition, a comparison of the two satellite data records is made against TCC observations at 22 meteorological stations in Europe, from the European Climate Assessment & Dataset (ECA&D). The results indicate generally larger ISCCP-H TCC with respect to the corresponding CLARA-A2 data, in particular in the Mediterranean. Compared to ECA&D data, both satellite datasets reveal a reasonable performance, with overall mean TCC biases of 2.1 and 5.2% for CLARA-A2 and ISCCP-H, respectively. This, along with the higher correlation coefficients between CLARA-A2 and ECA&D TCC, indicates the better performance of CLARA-A2 TCC data.
The metric tool code is designed for evaluating atmospheric rivers (AR) in climate models and reanalysis. It is python based, with built in metrics of AR frequency, AR precipitation, AR peak day, AR characteristics (width, length, area, latitude and longitude), and output of diagnostic plots. Is there
Mutual information as the asymptotic Bayesian measure of independence is an excellent starting point for investigating the existence of possible relationships among climate-relevant variables in large data sets, As mutual information is a nonlinear function of of its arguments, it is not beholden to the assumption of a linear relationship between the variables in question and can reveal features missed in linear correlation analyses. However, as mutual information is symmetric in its arguments, it only has the ability to reveal the probability that two variables are related. it provides no information as to how they are related; specifically, causal interactions or a relation based on a common cause cannot be detected. For this reason we also investigate the utility of a related quantity called the transfer entropy. The transfer entropy can be written as a difference between mutual informations and has the capability to reveal whether and how the variables are causally related. The application of these information theoretic measures is rested on some familiar examples using data from the International Satellite Cloud Climatology Project (ISCCP) to identify relation between global cloud cover and other variables, including equatorial pacific sea surface temperature (SST), over seasonal and El Nino Southern Oscillation (ENSO) cycles.
Detecting and tracking extreme events in gridded climatological data is a challenging problem on several fronts: algorithms, scalability, and I/O. Successful detection of these events will give climate scientists an alternate view of the behavior of different climatological variables, leading to enhanced scientific understanding of the impacts of events such as heat and cold waves, and on a larger scale, the El Nin o Southern Oscillation. Recent advances in computing power and research in data sciences enabled us to look at this problem with a different perspective from what was previously possible. In this paper we present our computationally efficient algorithms for anomalous cluster detection on climate change big data. We provide results on detection and tracking of surface temperature and geopotential height anomalies, a trend analysis, and a study of relationships between the variables. We also identify the limitations of our approaches, future directions for research and alternate approaches.
The urgent need to improve data delivery systems needed by scientists studying ocean role in climate and climate characteristics has been manifested in recent years because of the unprecedented climatic events experienced in many parts of the world. Indeed, there has been a striking and growing realization by governments and the general public indicating that national economies and human welfare depend on climate and its variability. In West and Central Africa, for instance climatic events, which have resulted in floods and droughts, have caused a lot of concern to both governments and people of the region. In particular, the droughts have been so widespread that greater awareness and concern have become generated for the need to find solutions to the problems created by the consequences of the climatic events. Particularly in the southern border regions of the Sahara Desert as well as in the Sahel region, the drought episodes considerably reduced food production and led to series of socioeconomic problems, not only in the areas affected by the droughts, but also in the other parts of West Africa. The various climatic variabilities which have caused the climatic events are no doubt related to the ocean-atmosphere interactions. Unfortunately, not much has been done on the understanding of these interactions, particularly as they affect developing countries. Indeed, not much has been done to develop programs which will reflect the general concerns and needs for researching into the ocean-atmosphere systems and their implications on man-environmental systems in many developing countries. This is for example, true of West and Central Africa, where compared with the middle latitude countries, much less is known about the characteristics of the ocean-atmosphere systems and their significance on man-environmental systems of the area.
Under the auspices of the Department of Education's No Child Left Behind (NCLB) Act, beginning in 2007 students will be tested in the science area. There are many techniques that educators can employ to teach students science. The use of authentic materials or in this case authentic data can be an engaging alternative to more traditional methods. An Earth science classroom is a great place for the integration of authentic data and science concepts. The National Aeronautics and Space Administration (NASA) has a wealth of high quality Earth science data available to the general public. For instance, the Atmospheric Science Data Center (ASDC) at NASA s Langley Research Center houses over 800 Earth science data sets related to Earth's radiation budget, clouds, aerosols and tropospheric chemistry. These data sets were produced to increase academic understanding of the natural and anthropogenic factors that influence global climate; however, a major hurdle in using authentic data is the size of the data and data documentation. To facilitate the use of these data sets for educational purposes, the Mentoring and inquirY using NASA Data on Atmospheric and Earth science for Teachers and Amateurs (MY NASA DATA) project has been established to systematically support educational activities at all levels of formal and informal education. The MY NASA DATA project accomplishes this by reducing these large data holdings to microsets that are easily accessible and explored by K-12 educators and students though the project's Web page. MY NASA DATA seeks to ease the difficulty in understanding the jargon-heavy language of Earth science. This manuscript will show how MY NASA DATA provides resources for NCLB implementation in the science area through an overview of the Web site, the different microsets available, the lesson plans and computer tools, and an overview of educational support mechanisms.
The gap between large-scale data production rate and the rate of generation of data-driven scientific insights has led to an analytical bottleneck in scientific domains like climate, biology, etc. This is primarily due to the lack of innovative analytical tools that can help scientists efficiently analyze and explore alternative hypotheses about the data, and communicate their findings effectively to a broad audience. In this paper, by reflecting on a set of successful collaborative research efforts between with a group of climate scientists and visualization researchers, we introspect how interactive visualization can help reduce the analytical bottleneck for domain scientists.
Provides a central location for viewing and comparing different methods for downscaling coarse resolution climate model output.
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Several algorithms were added to the Physical-space Statistical Analysis System (PSAS) from Goddard, which assimilates observational weather data by correcting for different levels of uncertainty about the data and different locations for mobile observation platforms. The new algorithms and use of the 512-node Intel Paragon allowed a hundred-fold decrease in processing time.
Current global analyses contain significant errors in primary hydrological fields such as precipitation, evaporation, and related cloud and moisture in the tropics. Work has been underway at NASA's Data Assimilation Office to explore the use of TRMM and SSM/I-derived rainfall and total precipitable water (TPW) data in global data assimilation to directly constrain these hydrological parameters. We found that assimilating these data types improves not only the precipitation and moisture estimates but also key climate parameters directly linked to convection such as the outgoing longwave radiation, clouds, and the large-scale circulation in the tropics. We will present results showing that assimilating TRMM and SSM/I 6-hour averaged rain rates and TPW estimates significantly reduces the state-dependent systematic errors in assimilated products. Specifically, rainfall assimilation improves cloud and latent heating distributions, which, in turn, improves the cloudy-sky radiation and the large-scale circulation, while TPW assimilation reduces moisture biases to improve radiation in clear-sky regions. Rainfall and TPW assimilation also improves tropical forecasts beyond 1 day.
Abstract Recent wildfires in the western United States have led to substantial economic losses and social stresses. There is a great concern that the new climatic state may further increase the intensity, duration, and frequency of wildfires. To examine temporal and spatial features of historical wildfire trends and future changes, a common fire index, the Keetch–Byram Drought Index (KBDI), is calculated over the contiguous United States (CONUS) and Alaska. We introduce an efficient initialization method and calculate 36 years (1982–2017) of historical KBDI at 4 km using a high-quality observational dataset. KBDIs are also calculated at 12 km using regional climate models and extended into the mid- and late-21st century. Based on the observational data, annual mean (and 95th percentile) KBDI over forested regions in the southwestern and northwestern CONUS has risen since 1982 at a rate of 5.2 (4.0) and 2.9 (3.3) per year, respectively, indicating a persistent drying trend fostering fire activities; the number of days exceeding the top 5% historical KBDI has increased by 16 and 25 d in the 36 years. Multiple regional climate model simulations project increasing wildfire potential and longer fire seasons over broader areas based on the estimated KBDI for the mid- and late-21st century. By the end of the century, most of the CONUS would experience about 90–189 more days per year exceeding the historical local maximum KBDI; areas with high KBDI (>600), whose occurrence correlates with large burned area, are projected to broaden by nearly 60 times in the southern CONUS. While both temperature and precipitation contribute to future KBDI changes, warming is the main driver of more frequent, intense and wide-spread extreme wildfires indicated by high KBDIs in future projections.
Earth system models (ESMs), which simulate the physics and chemistry of the global atmosphere, land, and ocean, are often used to generate future projections of climate change scenarios. These models are far too computationally inten- sive to run repeatedly, but limited sets of runs are insufficient for some important applications, like adequately sampling distribution tails to characterize extreme events. As a com- promise, emulators are substantially less expensive but may not have all of the complexity of an ESM. Here we demon- strate the use of a conditional generative adversarial network (GAN) to act as an ESM emulator. In doing so, we gain the ability to produce daily weather data that is consistent with what ESM might output over any chosen scenario. In particular, the GAN is aimed at representing a joint proba- bility distribution over space, time, and climate variables, enabling the study of correlated extreme events, such as floods, droughts, or heatwaves.
Passive microwave remote sensing at L-band (1.4 GHz) is sensitive to soil moisture and sea surface salinity, both important climate variables. Science studies involving these variables can now take advantage of new satellite L-band observations. The first mission with regular global passive microwave observations at L-band is the European Space Agency's Soil Moisture and Ocean Salinity (SMOS), launched November, 2009. A second mission, NASA's Aquarius, was launched June, 201 I. A third mission, NASA's Soil Moisture Active Passive (SMAP) is scheduled to launch in 2014. Together, these three missions may provide a decade-long data record-provided that they are intercalibrated. The intercalibration is best performed at the radiance (brightness temperature) level, and Antarctica is proving to be a key calibration target. However, Antarctica has thus far not been fully characterized as a potential target. This paper will present evaluations of Antarctica as a microwave calibration target for the above satellite missions. Preliminary analyses have identified likely target areas, such as the vicinity of Dome-C and larger areas within East Antarctica. Physical sources of temporal and spatial variability of polar firn are key to assessing calibration uncertainty. These sources include spatial variability of accumulation rate, compaction, surface characteristics (dunes, micro-topography), wind patterns, and vertical profiles of density and temperature. Using primarily SMOS data, variability is being empirically characterized and attempts are being made to attribute observed variability to physical sources. One expected outcome of these studies is the potential discovery of techniques for remotely sensing--over all of Antarctica-parameters such as surface temperature.
Effective pavement maintenance is essential for economic stability, optimal network performance, and roadway safety. Achieving this requires thorough evaluation of pavement conditions, including structural integrity, surface roughness, and distress characteristics. Pavement performance indicators play a critical role in influencing vehicle safety and ride quality. Recent advances have emphasized the use of data-driven modeling to anticipate pavement behavior, with the goal of optimizing resource allocation and refining Maintenance and Rehabilitation (M&R) strategies through accurate condition assessment. A foundational requirement for these modeling efforts is the availability of standardized, high-quality datasets that can support robust and reproducible infrastructure analysis. This data article presents a comprehensive dataset assembled to facilitate pavement performance prediction, with a geographic focus on Southeast Texas, particularly the flood-vulnerable area of Beaumont. The dataset encompasses pavement and traffic attributes, meteorological records, flood simulation outputs, ground deformation measurements, and topographic indices, enabling detailed examination of both load-associated and non-load-associated degradation mechanisms. Data preprocessing was performed using ArcGIS Pro, Microsoft Excel, and Python to ensure consistency and usability in data-driven modeling applications, including machine learning workflows. Key contributions of this dataset include its utility in analyzing the climatic and environmental factors affecting pavement conditions, identifying critical predictive features, and enabling in-depth correlation analysis across diverse variables. By filling existing gaps in input variable selection resources, this dataset supports the development of predictive tools for estimating future maintenance demand and enhancing the resilience of pavement networks in flood-impacted areas. The resource highlights the importance of standardized datasets for advancing pavement management practices and provides a robust foundation for ongoing infrastructure performance modeling.
Data were collected to characterize whole-house mechanical ventilation (WHMV) and indoor air quality (IAQ) in 55 homes in the Marine climate of Oregon and Cold-Dry climate of Colorado in the U.S. Sixteen homes were monitored for two weeks, with and without WHMV operating. Ventilation airflows; airtightness; time-resolved CO2, PM2.5 and radon; and time-integrated NO2, NOX and formaldehyde were measured. Participants provided information about IAQ-impacting activities, perceptions and ventilation use. All homes had operational cooktop ventilation and bathroom exhaust. Thirty homes had equipment that could meet the ASHRAE 62.2-2010 standard with continuous or controlled runtime and 34 had some WHMV operating as found. Thirty-five of 46 participants with WHMV reported they did not know how to operate it, and only half of the systems were properly labeled. Two-week homes had lower formaldehyde, radon, CO2, and NO (NOX-NO2) when operated with WHMV; and also had faster PM2.5 decays following indoor emission events. Overall IAQ satisfaction was similar in Oregon and Colorado, but more Colorado participants (19 vs. 3%) felt their IAQ could be improved and more reported dryness as a problem (58 vs. 14%). The collected data indicate that there are benefits of operating WHMV, even when continuous use may not be needed because outdoor pollutant concentrations are low and indoor sources do not present substantial challenges.
Abstract The divergence among Earth system models in the terrestrial carbon cycle has prompted interest in how to reduce uncertainty. Previous studies have identified model structural uncertainty arising from process parameterizations and parameter values. The current study highlights the importance of climate forcing in generating carbon cycle uncertainty. We use simulations in which three models (Community Land Model version 4 (CLM4), CLM4.5, CLM5) with substantially different carbon cycles are forced with two climate reconstructions (CRUNCEPv7, Global Soil Wetness Project 3 version 1 (GSWP3v1)) to examine the contributions of model structure and climate to uncertainty in the carbon cycle over the period 1850–2014. Climate uncertainty for global annual net biome production exceeds one third of total uncertainty (defined as the sum of climate and model structure uncertainty) in the first half of the twentieth century, but declines after the 1950s. Global annual gross primary productivity, net primary productivity, heterotrophic respiration, and vegetation and soil carbon stocks have substantial climate uncertainty (relative to total uncertainty) throughout the simulation period. Climate forcing contributes more than one half of total uncertainty for these carbon cycle fluxes and stocks throughout boreal North America and Eurasia, some midlatitude regions, and in eastern Amazonia and western equatorial Africa during the decade 2000–2009. Comparison with observationally based data sets of the carbon cycle using model benchmarking methods provides insight into strengths and deficiencies among models and climate forcings, but we caution against overreliance on benchmarking to discriminate among models. The conceptualization of uncertainty arising from this study implies embracing multiple feasible model simulations rather than focusing on which model or simulation is best.