Satellite Climate Data Research Articles

Observation of TEC perturbation associated with medium‐scale traveling ionospheric disturbance and possible seeding mechanism of atmospheric gravity wave at a Brazilian sector

AbstractIn the present study, we document daytime total electron content (TEC) disturbances associated with medium‐scale traveling ionospheric disturbances (MSTIDs), on few chosen geomagnetically quiet days over Southern Hemisphere of Brazilian longitude sector. These disturbances are derived from TEC data obtained using Global Navigation Satellite System (GNSS) receiver networks. From the keograms and cross‐correlation maps, the TEC disturbances are identified as the MSTIDs that are propagating equatorward‐eastward, having most of their average wavelengths longer in latitude than in longitude direction. These are the important outcomes of the present study which suggest that the daytime MSTIDs over Southern Hemisphere are similar to their counterparts in the Northern Hemisphere. Another important outcome is that the occurrence characteristics of these MSTIDs and that of atmospheric gravity wave (AGW) activities in the thermosphere are found to be similar on day‐to‐day basis. This suggests a possible connection between them, confirming the widely accepted AGW forcing mechanism for the generation of these daytime MSTIDs. The source of this AGW is investigated using the Geostationary Operational Environmental Satellite system (GOES) and Constellation Observing System for Meteorology, Ionosphere, and Climate satellite data. Finally, we provided evidences that AGWs are generated by convection activities from the tropospheric region.

The summer diurnal cycle of coastal cloudiness over west Iberia using Meteosat/SEVIRI and a WRF regional climate model simulation

The summer time cloud diurnal cycle over western Iberia is analysed here using a satellite climate data record of fractional cloud cover based on 9 years of Meteosat Second Generation observations which is distributed by the EUMETSAT's Climate Monitoring Satellite Applications Facility. These observations were complemented with a corresponding mean cloud diurnal cycle using SYNOP reports on six locations over the studied domain. It is shown that the main coastal mountain range separates regions that are characterized by two very different cloud regimes: stratocumulus-topped boundary layer convection dominates the region towards the coast and continental cumulus convection dominates the region to the east of these mountains. To explain the observed variability, a long-term regional climate model [Weather Research and Forecasting model (WRF)] simulation over Iberia was used. A comparison of the observations against model output for the common period between observations and simulation shows that although the model generally underestimates cloudiness, it is able to represent the diurnal cycle in a realistic manner. It is shown that the observed cloud diurnal evolution is linked to the thermal circulations generated by the land-sea contrast and orography. The extent to which the cloud deck penetrates inland is closely related to the coastal orography: although smaller hills tend to enhance cloudiness, larger mountains block the progression of the marine boundary layer further inland, as it behaves as a density current. Larger mountains also produce katabatic flow and a rather strong subsidence aloft during the night. The warming due to this subsidence helps the blocking of the cloud deck as it is partially responsible for evaporating clouds, as shown by a potential temperature budget analysis.

New satellite climate data records indicate strong coupling between recent frozen season changes and snow cover over high northern latitudes

We examined new satellite climate data records documenting frozen (FR) season and snow cover extent (SCE) changes from 1979 to 2011 over all northern vegetated land areas (≥45 °N). New insight on the spatial and temporal characteristics of seasonal FR ground and snowpack melt changes were revealed by integrating the independent FR and SCE data records. Similar decreasing trends in annual FR and SCE durations coincided with widespread warming (0.4 °C decade−1). Relatively strong declines in FR and SCE durations in spring and summer are partially offset by increasing trends in fall and winter. These contrasting seasonal trends result in relatively weak decreasing trends in annual FR and SCE durations. A dominant SCE retreat response to FR duration decreases was observed, while the sign and strength of this relationship was spatially complex, varying by latitude and regional snow cover, and climate characteristics. The spatial extent of FR conditions exceeds SCE in early spring and is smaller during snowmelt in late spring and early summer, while FR ground in the absence of snow cover is widespread in the fall. The integrated satellite record, for the first time, reveals a general increasing trend in annual snowmelt duration from 1.3 to 3.3 days decade−1 (p < 0.01), occurring largely in the fall. Annual FR ground durations are declining from 0.8 to 1.3 days decade−1. These changes imply extensive biophysical impacts to regional snow cover, soil and permafrost regimes, surface water and energy budgets, and climate feedbacks, while ongoing satellite microwave missions provide an effective means for regional monitoring.

A long-term Northern Hemisphere snow cover extent data record for climate studies and monitoring

Abstract. This paper describes the long-term, satellite-based visible snow cover extent National Oceanic and Atmospheric Administration (NOAA) climate data record (CDR) currently available for climate studies, monitoring, and model validation. This environmental data product is developed from weekly Northern Hemisphere snow cover extent data that have been digitized from snow cover maps onto a Cartesian grid draped over a polar stereographic projection. The data have a spatial resolution of 190.6 km at 60° latitude, are updated monthly, and span the period from 4 October 1966 to the present. The data comprise the longest satellite-based CDR of any environmental variable. Access to the data is provided in Network Common Data Form (netCDF) and archived by NOAA's National Climatic Data Center (NCDC) under the satellite Climate Data Record Program (doi:10.7289/V5N014G9). The basic characteristics, history, and evolution of the data set are presented herein. In general, the CDR provides similar spatial and temporal variability to its widely used predecessor product. Key refinements included in the CDR improve the product's grid accuracy and documentation and bring metadata into compliance with current standards for climate data records.

Assessing the Effects of Climate and Socioeconomic Factors on Vulnerability to Vector-Borne Diseases in Latin America

Climate change is imposing a large burden on the most vulnerable populations, particularly in the developing world. Establishing consistent causal relationships, however, is difficult because a multiplicity of climatic, economic and sociodemographic elements are combined to create the conditions for an outbreak of vector-borne disease. Based on a two-step procedure, this paper presents and tests an approach to estimating the effects of epidemic outbreaks on health vulnerability. The model proposed is empirically tested for five countries in Latin America where dengue is a national health priority. Using data from national censuses, satellite climate information and data from a newly developed disease outbreak surveillance online platform, the paper finds that climate has nonnegligible effects on health vulnerability. The evidence found and the vulnerability index constructed can be used to analyze the main determinants of vulnerability in order to address policy concerns.

Satellite Regional Cloud Climatology over the Great Lakes

Thirty-one years of imager data from polar orbiting satellites are composited to produce a satellite climate data set of cloud amount for the Great Lakes region. A trend analysis indicates a slight decreasing trend in cloud cover over the region during this time period. The trend is significant and largest (~2% per decade) over the water bodies. A strong seasonal cycle of cloud cover is observed over both land and water surfaces. Winter cloud amounts are greater over the water bodies than land due to heat and moisture flux into the atmosphere. Late spring through early autumn cloud amounts are lower over the water bodies than land due to stabilization of the boundary layer by relatively cooler lake waters. The influence of the lakes on cloud cover also extends beyond their shores, affecting cloud cover and properties far down wind. Cloud amount composited by wind direction demonstrate that the increasing cloud amounts downwind of the lakes is greatest during autumn and winter. Cold air flows over relatively warm lakes in autumn and winter generate wind parallel convective cloud bands. The cloud properties of these wind parallel cloud bands over the lakes during winter are presented.

A long-term and reproducible passive microwave sea ice concentration data record for climate studies and monitoring

Abstract. A long-term, consistent, and reproducible satellite-based passive microwave sea ice concentration climate data record (CDR) is available for climate studies, monitoring, and model validation with an initial operation capability (IOC). The daily and monthly sea ice concentration data are on the National Snow and Ice Data Center (NSIDC) polar stereographic grid with nominal 25 km × 25 km grid cells in both the Southern and Northern Hemisphere polar regions from 9 July 1987 to 31 December 2007. The data files are available in the NetCDF data format at http://nsidc.org/data/g02202.html and archived by the National Climatic Data Center (NCDC) of the National Oceanic and Atmospheric Administration (NOAA) under the satellite climate data record program (http://www.ncdc.noaa.gov/cdr/operationalcdrs.html). The description and basic characteristics of the NOAA/NSIDC passive microwave sea ice concentration CDR are presented here. The CDR provides similar spatial and temporal variability as the heritage products to the user communities with the additional documentation, traceability, and reproducibility that meet current standards and guidelines for climate data records. The data set, along with detailed data processing steps and error source information, can be found at http://dx.doi.org/10.7265/N55M63M1.

Recent snowfall anomalies in Dronning Maud Land, East Antarctica, in a historical and future climate perspective

Enhanced snowfall on the East Antarctic ice sheet is projected to significantly mitigate 21st century global sea level rise. In recent years (2009 and 2011), regionally extreme snowfall anomalies in Dronning Maud Land, in the Atlantic sector of East Antarctica, have been observed. It has been unclear, however, whether these anomalies can be ascribed to natural decadal variability, or whether they could signal the beginning of a long‐term increase of snowfall. Here we use output of a regional atmospheric climate model, evaluated with available firn core records and gravimetry observations, and show that such episodes had not been seen previously in the satellite climate data era (1979). Comparisons with historical data that originate from firn cores, one with records extending back to the 18th century, confirm that accumulation anomalies of this scale have not occurred in the past ~60 years, although comparable anomalies are found further back in time. We examined several regional climate model projections, describing various warming scenarios into the 21st century. Anomalies with magnitudes similar to the recently observed ones were not present in the model output for the current climate, but were found increasingly probable toward the end of the 21st century.

A global model of malaria climate sensitivity: comparing malaria response to historic climate data based on simulation and officially reported malaria incidence.

BackgroundThe role of the Anopheles vector in malaria transmission and the effect of climate on Anopheles populations are well established. Models of the impact of climate change on the global malaria burden now have access to high-resolution climate data, but malaria surveillance data tends to be less precise, making model calibration problematic. Measurement of malaria response to fluctuations in climate variables offers a way to address these difficulties. Given the demonstrated sensitivity of malaria transmission to vector capacity, this work tests response functions to fluctuations in land surface temperature and precipitation.MethodsThis study of regional sensitivity of malaria incidence to year-to-year climate variations used an extended Macdonald Ross compartmental disease model (to compute malaria incidence) built on top of a global Anopheles vector capacity model (based on 10 years of satellite climate data). The predicted incidence was compared with estimates from the World Health Organization and the Malaria Atlas. The models and denominator data used are freely available through the Eclipse Foundation’s Spatiotemporal Epidemiological Modeller (STEM).ResultsAlthough the absolute scale factor relating reported malaria to absolute incidence is uncertain, there is a positive correlation between predicted and reported year-to-year variation in malaria burden with an averaged root mean square (RMS) error of 25% comparing normalized incidence across 86 countries. Based on this, the proposed measure of sensitivity of malaria to variations in climate variables indicates locations where malaria is most likely to increase or decrease in response to specific climate factors. Bootstrapping measures the increased uncertainty in predicting malaria sensitivity when reporting is restricted to national level and an annual basis. Results indicate a potential 20x improvement in accuracy if data were available at the level ISO 3166–2 national subdivisions and with monthly time sampling.ConclusionsThe high spatial resolution possible with state-of-the-art numerical models can identify regions most likely to require intervention due to climate changes. Higher-resolution surveillance data can provide a better understanding of how climate fluctuations affect malaria incidence and improve predictions. An open-source modelling framework, such as STEM, can be a valuable tool for the scientific community and provide a collaborative platform for developing such models.

Analysis on Relationship between Lake Change and Climate Change in the Ulan Buh Desert

In recent years,the lakes in desert have been significant changed. Based on the satellite data and climate data,this paper analyzes the variation of two lakes in the Ulan Buh Desert. Using CBERS CCD data during the period from 2000 to 2008,the best wave bands were chosen to process the image,such as the high-accuracy geometric registration and mosaic,the brim information was extracted,and the change figures of the lakes in the Ulan Buh Desert were charted. A spectral reflection curve was charted with a TM image in 2000 to identify the changed area of the lakes. In order to avoid the contingency,the areas of each lake in recent continuous years were used to calculate the average area of each lake. The correlation coefficients between lake areas and climate change in recent 20 years were calculated. The result shows that the trend was accordant well with the result of remote sensing detection,and there was a significant positive correlation between the lake area and the annual precipitation. The lakes with small area were more sensitive to climate change than those with large area. The study result shows that remote sensing is of high usability in monitoring the lakes in desert,especially in west Inner Mongolia.

Achieving Subpixel Georeferencing Accuracy in the Canadian AVHRR Processing System

Precise geolocation is one of the fundamental requirements for satellite imagery to be suitable for climate applications. The Global Climate Observing System and the Committee on Earth Observing Satellites identified the requirement for the accuracy of geolocation of satellite data for climate applications as 1/3 field of view (FOV). This requirement for the series of the Advanced Very High Resolution Radiometer (AVHRR) on the National Oceanic and Atmospheric Administration platforms cannot be met without implementing the ground control point (GCP) correction, particularly for historical data, because of limited accuracy of orbit modeling and knowledge of satellite attitude angles. This paper presents a new method for precise georeferencing of the AVHRR imagery developed as part of the new Canadian AVHRR processing system (CAPS) designed for generating high-quality AVHRR satellite climate data record at 1-km spatial resolution. The method works in swath projection and uses the following: 1) the reference monthly images from Moderate Resolution Imaging Spectroradiometer at 250-m resolution; 2) orthorectification to correct for surface elevation; and 3) a novel image matching technique in swath projection to achieve the subpixel resolution. The method is designed for processing daytime data as it intensively employs observations from optical solar bands, the near-infrared channel in particular. The application of the developed processing system showed that the algorithm achieved better than 1/3 FOV geolocation accuracy for AVHRR 1-km scenes. It has very high efficiency rate (> 97%) due to the dense and uniform GCP coverage of the study area (5700 × 4800 km2 ), covering the entire Canada, the Northern U.S., Alaska, Greenland, and surrounding oceans.

Impact of orthorectification and spatial sampling on maximum NDVI composite data in mountain regions

Topography and accuracy of image geometric registration significantly affect the quality of satellite data, since pixels are displaced depending on surface elevation and viewing geometry. This effect should be corrected for through the process of accurate image navigation and orthorectification in order to meet the geolocation accuracy for systematic observations specified by the Global Climate Observing System (GCOS) requirements for satellite climate data records. We investigated the impact of orthorectification on the accuracy of maximum Normalized Difference Vegetation Index (NDVI) composite data for a mountain region in north-western Canada at various spatial resolutions (1 km, 4 km, 5 km, and 8 km). Data from AVHRR on board NOAA-11 (1989 and 1990) and NOAA-16 (2001, 2002, and 2003) processed using a system called CAPS (Canadian AVHRR Processing System) for the month of August were considered. Results demonstrate the significant impact of orthorectification on the quality of composite NDVI data in mountainous terrain. Differences between orthorectified and non-orthorectified NDVI composites (ΔNDVI) adopted both large positive and negative values, with the 1% and 99% percentiles of ΔNDVI at 1 km resolution spanning values between − 0.16 < ΔNDVI < 0.09. Differences were generally reduced to smaller numbers for coarser resolution data, but systematic positive biases for non-orthorectified composites were obtained at all spatial resolutions, ranging from 0.02 (1 km) to 0.004 (8 km). Analyzing the power spectra of maximum NDVI composites at 1 km resolution, large differences between orthorectified and non-orthorectified AVHRR data were identified at spatial scales between 4 km and 10 km. Validation of NOAA-16 AVHRR NDVI with MODIS NDVI composites revealed higher correlation coefficients (by up to 0.1) for orthorectified composites relative to the non-orthorectified case. Uncertainties due to the AVHRR Global Area Coverage (GAC) sampling scheme introduce an average positive bias of 0.02 ± 0.03 at maximum NDVI composite level that translates into an average relative bias of 10.6% ± 19.1 for sparsely vegetated mountain regions. This can at least partially explain the systematic average positive biases we observed relative to our results in AVHRR GAC-based composites from the Global Inventory Modeling and Mapping Studies (GIMMS) and Polar Pathfinder (PPF) datasets (0.19 and 0.05, respectively). With regard to the generation of AVHRR long-term climate data records, results suggest that orthorectification should be an integral part of AVHRR pre-processing, since neglecting the terrain displacement effect may lead to important biases and additional noise in time series at various spatial scales.

Long-Term Surveillance Data and Patterns of Invasion by Aedes albopictus in Florida

We used geographic information system (GIS) and long-term mosquito surveillance data from Lake, Pasco, Manatee, and Sarasota Counties, FL, to look at patterns of invasion by Aedes albopictus and concurrent changes in resident Ae. aegypti. We investigated environmental factors associated with population changes in these species with the use of satellite climate data. Aedes aegypti densities attenuated rapidly following the arrival of Ae. albopictus in most counties, yet both species persisted in equilibrium in Manatee County. We discuss the relative importance of rainfall, habitat, and proximity to urban areas in the population dynamics of these species in sympatry.