Relevant bibliographies by topics / Satellite estimates

Academic literature on the topic 'Satellite estimates'

Author: Grafiati

Published: 7 July 2024

Last updated: 7 July 2024

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Satellite estimates.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles
Dissertations / Theses
Books
Book chapters
Conference papers
Reports

Journal articles on the topic "Satellite estimates":

Smith, Thomas M., Phillip A. Arkin, John J. Bates, and George J. Huffman. "Estimating Bias of Satellite-Based Precipitation Estimates." Journal of Hydrometeorology 7, no. 5 (October 1, 2006): 841–56. http://dx.doi.org/10.1175/jhm524.1.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Abstract Systematic biases in satellite-based precipitation estimates can be the dominant component of their uncertainty. These biases may not be reduced by averaging, which makes their evaluation particularly important. Described here are several methods of evaluating the biases and their characteristics. Methods are developed and tested using monthly average precipitation estimates from several satellites. Direct estimates of bias are obtained from analysis of satellite–gauge estimates, and they indicate the general bias patterns and magnitudes over land. Direct estimates cannot be computed over the oceans, so indirect-bias estimates based on ensembles of satellite and gauge estimates are also developed. These indirect estimates are consistent with direct estimates in locations where they can be compared, while giving near-global coverage. For both bias estimates computed here, the bias uncertainty is higher than nonsystematic error estimates, caused by random or sampling errors and which have been previously reported by others for satellite estimates. Because of their greater spatial coverage, indirect-bias estimates are preferable for bias adjustment of satellite-based precipitation. The adjustment methods developed reduce the bias associated with each satellite while estimating the remaining bias uncertainty for the satellite. By adjusting all satellites to a consistent base, the bias adjustments also minimize artificial climate-scale variations in analyses that could be caused by the addition or removal of satellite products as their availability changes.

Zhang, Hai, Zigang Wei, Barron H. Henderson, Susan C. Anenberg, Katelyn O’Dell, and Shobha Kondragunta. "Nowcasting Applications of Geostationary Satellite Hourly Surface PM2.5 Data." Weather and Forecasting 37, no. 12 (December 2022): 2313–29. http://dx.doi.org/10.1175/waf-d-22-0114.1.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Abstract The mass concentration of fine particulate matter (PM2.5; diameters less than 2.5 μm) estimated from geostationary satellite aerosol optical depth (AOD) data can supplement the network of ground monitors with high temporal (hourly) resolution. Estimates of PM2.5 over the United States were derived from NOAA’s operational geostationary satellites’ Advanced Baseline Imager (ABI) AOD data using a geographically weighted regression with hourly and daily temporal resolution. Validation versus ground observations shows a mean bias of −21.4% and −15.3% for hourly and daily PM2.5 estimates, respectively, for concentrations ranging from 0 to 1000 μg m−3. Because satellites only observe AOD in the daytime, the relation between observed daytime PM2.5 and daily mean PM2.5 was evaluated using ground measurements; PM2.5 estimated from ABI AODs were also examined to study this relationship. The ground measurements show that daytime mean PM2.5 has good correlation (r > 0.8) with daily mean PM2.5 in most areas of the United States, but with pronounced differences in the western United States due to temporal variations caused by wildfire smoke; the relation between the daytime and daily PM2.5 estimated from the ABI AODs has a similar pattern. While daily or daytime estimated PM2.5 provides exposure information in the context of the PM2.5 standard (>35 μg m−3), the hourly estimates of PM2.5 used in nowcasting show promise for alerts and warnings of harmful air quality. The geostationary satellite based PM2.5 estimates inform the public of harmful air quality 10 times more than standard ground observations (1.8 versus 0.17 million people per hour). Significance Statement Fine particulate matter (PM2.5; diameters less than 2.5 μm) are generated from smoke, dust, and emissions from industrial, transportation, and other sectors. They are harmful to human health and even lead to premature mortality. Data from geostationary satellites can help estimate surface PM2.5 exposure by filling in gaps that are not covered by ground monitors. With this information, people can plan their outdoor activities accordingly. This study shows that availability of hourly PM2.5 observations covering the entire continental United States is more informative to the public about harmful exposure to pollution. On average, 1.8 million people per hour can be informed using satellite data compared to 0.17 million people per hour based on ground observations alone.

Itkin, M., and A. Loew. "Multi-satellite rainfall sampling error estimates – a comparative study." Hydrology and Earth System Sciences Discussions 9, no. 10 (October 12, 2012): 11677–706. http://dx.doi.org/10.5194/hessd-9-11677-2012.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Abstract. This study focus is set on quantifying sampling related uncertainty in the satellite rainfall estimates. We conduct observing system simulation experiment to estimate sampling error for various constellations of Low-Earth orbiting and geostationary satellites. There are two types of microwave instruments currently available: cross track sounders and conical scanners. We evaluate the differences in sampling uncertainty for various satellite constellations that carry instruments of the common type as well as in combination with geostationary observations. A precise orbital model is used to simulate realistic satellite overpasses with orbital shifts taken into account. With this model we resampled rain gauge timeseries to simulate satellites rainfall estimates free of retrieval and calibration errors. We concentrate on two regions, Germany and Benin, areas with different precipitation regimes. Our results show that sampling uncertainty for all satellite constellations does not differ greatly depending on the area despite the differences in local precipitation patterns. Addition of 3 hourly geostationary observations provides equal performance improvement in Germany and Benin, reducing rainfall undersampling by 20–25% of the total rainfall amount. Authors do not find a significant difference in rainfall sampling between conical imager and cross-track sounders.

Bowman, Kenneth P., Cameron R. Homeyer, and Dalon G. Stone. "A Comparison of Oceanic Precipitation Estimates in the Tropics and Subtropics." Journal of Applied Meteorology and Climatology 48, no. 7 (July 1, 2009): 1335–44. http://dx.doi.org/10.1175/2009jamc2149.1.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Abstract A number of Earth remote sensing satellites are currently carrying passive microwave radiometers. A variety of different retrieval algorithms are used to estimate surface rain rates over the ocean from the microwave radiances observed by the radiometers. This study compares several different satellite algorithms with each other and with independent data from rain gauges on ocean buoys. The rain gauge data are from buoys operated by the NOAA Pacific Marine Environmental Laboratory. Potential errors and biases in the gauge data are evaluated. Satellite data are from the Tropical Rainfall Measuring Mission Microwave Imager and from the Special Sensor Microwave Imager instruments on the operational Defense Meteorological Satellite Program F13, F14, and F15 satellites. These data have been processed into rain-rate estimates by the NASA Precipitation Measurement Mission and by Remote Sensing Systems, Inc. Biases between the different datasets are estimated by computing differences between long-term time averages. Most of the satellite datasets agree with each other, and with the gauge data, to within 10% or less. The biases tend to be proportional to the mean rain rate, but the geographical patterns of bias vary depending on the choice of data source and algorithm. Some datasets, however, show biases as large as about 25%, so care should be taken when using these data for climatological studies.

Tian, Yudong, Christa D. Peters-Lidard, Robert F. Adler, Takuji Kubota, and Tomoo Ushio. "Evaluation of GSMaP Precipitation Estimates over the Contiguous United States." Journal of Hydrometeorology 11, no. 2 (April 1, 2010): 566–74. http://dx.doi.org/10.1175/2009jhm1190.1.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Abstract Precipitation estimates from the Global Satellite Mapping of Precipitation (GSMaP) project are evaluated over the contiguous United States (CONUS) for the period of 2005–06. GSMaP combines precipitation retrievals from the Tropical Rainfall Measuring Mission satellite and other polar-orbiting satellites, and interpolates them with cloud motion vectors derived from infrared images from geostationary satellites, to produce a high-resolution dataset. Four other satellite-based datasets are also evaluated concurrently with GSMaP, to provide a better perspective. The new Climate Prediction Center (CPC) unified gauge analysis is used as the reference data. The evaluation shows that GSMaP does well in capturing the spatial patterns of precipitation, especially for summer, and that it has better estimation of precipitation amount over the eastern than over the western CONUS. Meanwhile, GSMaP shares many of the challenges common to other satellite-based products, including that it underestimates in winter and overestimates in summer. In winter, GSMaP has on average one-half less precipitation over the western region and one-third less over the eastern region, whereas in summer it has about three-quarters and one-quarter more estimated precipitation over the two respective regions, respectively. Most of the summer overestimates (winter underestimates) are from an excessive (insufficient) number of strong events (>20 mm day−1). Overall, GSMaP’s performance is comparable to other satellite-based products, with slightly better probability of detection during summer, and the different satellite-based estimates as a group have better agreement among themselves during summer than during winter.

Konings, Alexandra G., A. Anthony Bloom, Junjie Liu, Nicholas C. Parazoo, David S. Schimel, and Kevin W. Bowman. "Global satellite-driven estimates of heterotrophic respiration." Biogeosciences 16, no. 11 (June 4, 2019): 2269–84. http://dx.doi.org/10.5194/bg-16-2269-2019.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Abstract. While heterotrophic respiration (Rh) makes up about a quarter of gross global terrestrial carbon fluxes, it remains among the least-observed carbon fluxes, particularly outside the midlatitudes. In situ measurements collected in the Soil Respiration Database (SRDB) number only a few hundred worldwide. Similarly, only a single data-driven wall-to-wall estimate of annual average heterotrophic respiration exists, based on bottom-up upscaling of SRDB measurements using an assumed functional form to account for climate variability. In this study, we exploit recent advances in remote sensing of terrestrial carbon fluxes to estimate global variations in heterotrophic respiration in a top-down fashion at monthly temporal resolution and 4∘×5∘ spatial resolution. We combine net ecosystem productivity estimates from atmospheric inversions of the NASA Carbon Monitoring System-Flux (CMS-Flux) with an optimally scaled gross primary productivity dataset based on satellite-observed solar-induced fluorescence variations to estimate total ecosystem respiration as a residual of the terrestrial carbon balance. The ecosystem respiration is then separated into autotrophic and heterotrophic components based on a spatially varying carbon use efficiency retrieved in a model–data fusion framework (the CARbon DAta MOdel fraMework, CARDAMOM). The resulting dataset is independent of any assumptions about how heterotrophic respiration responds to climate or substrate variations. It estimates an annual average global average heterotrophic respiration flux of 43.6±19.3 Pg C yr−1. Sensitivity and uncertainty analyses showed that the top-down Rh are more sensitive to the choice of input gross primary productivity (GPP) and net ecosystem productivity (NEP) datasets than to the assumption of a static carbon use efficiency (CUE) value, with the possible exception of the wet tropics. These top-down estimates are compared to bottom-up estimates of annual heterotrophic respiration, using new uncertainty estimates that partially account for sampling and model errors. Top-down heterotrophic respiration estimates are higher than those from bottom-up upscaling everywhere except at high latitudes and are 30 % greater overall (43.6 Pg C yr−1 vs. 33.4 Pg C yr−1). The uncertainty ranges of both methods are comparable, except poleward of 45∘ N, where bottom-up uncertainties are greater. The ratio of top-down heterotrophic to total ecosystem respiration varies seasonally by as much as 0.6 depending on season and climate, illustrating the importance of studying the drivers of autotrophic and heterotrophic respiration separately, and thus the importance of data-driven estimates of Rh such as those estimated here.

Utsumi, Nobuyuki, Hyungjun Kim, F. Joseph Turk, and Ziad S. Haddad. "Improving Satellite-Based Subhourly Surface Rain Estimates Using Vertical Rain Profile Information." Journal of Hydrometeorology 20, no. 5 (May 1, 2019): 1015–26. http://dx.doi.org/10.1175/jhm-d-18-0225.1.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Abstract Quantifying time-averaged rain rate, or rain accumulation, on subhourly time scales is essential for various application studies requiring rain estimates. This study proposes a novel idea to estimate subhourly time-averaged surface rain rate based on the instantaneous vertical rain profile observed from low-Earth-orbiting satellites. Instantaneous rain estimates from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) are compared with 1-min surface rain gauges in North America and Kwajalein atoll for the warm seasons of 2005–14. Time-lagged correlation analysis between PR rain rates at various height levels and surface rain gauge data shows that the peak of the correlations tends to be delayed for PR rain at higher levels up to around 6-km altitude. PR estimates for low to middle height levels have better correlations with time-delayed surface gauge data than the PR’s estimated surface rain rate product. This implies that rain estimates for lower to middle heights may have skill to estimate the eventual surface rain rate that occurs 1–30 min later. Therefore, in this study, the vertical profiles of TRMM PR instantaneous rain estimates are averaged between the surface and various heights above the surface to represent time-averaged surface rain rate. It was shown that vertically averaged PR estimates up to middle heights (~4.5 km) exhibit better skill, compared to the PR estimated instantaneous surface rain product, to represent subhourly (~30 min) time-averaged surface rain rate. These findings highlight the merit of additional consideration of vertical rain profiles, not only instantaneous surface rain rate, to improve subhourly surface estimates of satellite-based rain products.

Gerbi, Gregory P., Emmanuel Boss, P. Jeremy Werdell, Christopher W. Proctor, Nils Haëntjens, Marlon R. Lewis, Keith Brown, et al. "Validation of Ocean Color Remote Sensing Reflectance Using Autonomous Floats." Journal of Atmospheric and Oceanic Technology 33, no. 11 (November 2016): 2331–52. http://dx.doi.org/10.1175/jtech-d-16-0067.1.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

AbstractThe use of autonomous profiling floats for observational estimates of radiometric quantities in the ocean is explored, and the use of this platform for validation of satellite-based estimates of remote sensing reflectance in the ocean is examined. This effort includes comparing quantities estimated from float and satellite data at nominal wavelengths of 412, 443, 488, and 555 nm, and examining sources and magnitudes of uncertainty in the float estimates. This study had 65 occurrences of coincident high-quality observations from floats and MODIS Aqua and 15 occurrences of coincident high-quality observations floats and Visible Infrared Imaging Radiometer Suite (VIIRS). The float estimates of remote sensing reflectance are similar to the satellite estimates, with disagreement of a few percent in most wavelengths. The variability of the float–satellite comparisons is similar to the variability of in situ–satellite comparisons using a validation dataset from the Marine Optical Buoy (MOBY). This, combined with the agreement of float-based and satellite-based quantities, suggests that floats are likely a good platform for validation of satellite-based estimates of remote sensing reflectance.

Dietrich, S., D. Casella, F. Di Paola, M. Formenton, A. Mugnai, and P. Sanò. "Lightning-based propagation of convective rain fields." Natural Hazards and Earth System Sciences 11, no. 5 (May 27, 2011): 1571–81. http://dx.doi.org/10.5194/nhess-11-1571-2011.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Abstract. This paper describes a new multi-sensor approach for continuously monitoring convective rain cells. It exploits lightning data from surface networks to propagate rain fields estimated from multi-frequency brightness temperature measurements taken by the AMSU/MHS microwave radiometers onboard NOAA/EUMETSAT low Earth orbiting operational satellites. Specifically, the method allows inferring the development (movement, morphology and intensity) of convective rain cells from the spatial and temporal distribution of lightning strokes following any observation by a satellite-borne microwave radiometer. Obviously, this is particularly attractive for real-time operational purposes, due to the sporadic nature of the low Earth orbiting satellite measurements and the continuous availability of ground-based lightning measurements – as is the case in most of the Mediterranean region. A preliminary assessment of the lightning-based rainfall propagation algorithm has been successfully made by using two pairs of consecutive AMSU observations, in conjunction with lightning measurements from the ZEUS network, for two convective events. Specifically, we show that the evolving rain fields, which are estimated by applying the algorithm to the satellite-based rainfall estimates for the first AMSU overpass, show an overall agreement with the satellite-based rainfall estimates for the second AMSU overpass.

Li, Min, and Yunbin Yuan. "Estimation and Analysis of the Observable-Specific Code Biases Estimated Using Multi-GNSS Observations and Global Ionospheric Maps." Remote Sensing 13, no. 16 (August 5, 2021): 3096. http://dx.doi.org/10.3390/rs13163096.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Observable-specific bias (OSB) parameterization allows observation biases belonging to various signal types to be flexibly addressed in the estimation of ionosphere and global navigation satellite system (GNSS) clock products. In this contribution, multi-GNSS OSBs are generated by two different methods. With regard to the first method, geometry-free (GF) linear combinations of the pseudorange and carrier-phase observations of a global multi-GNSS receiver network are formed for the extraction of OSB observables, and global ionospheric maps (GIMs) are employed to correct ionospheric path delays. Concerning the second method, satellite and receiver OSBs are converted directly from external differential code bias (DCB) products. Two assumptions are employed in the two methods to distinguish satellite- and receiver-specific OSB parameters. The first assumption is a zero-mean condition for each satellite OSB type and GNSS signal. The second assumption involves ionosphere-free (IF) linear combination signal constraints for satellites and receivers between two signals, which are compatible with the International GNSS Service (IGS) clock product. Agreement between the multi-GNSS satellite OSBs estimated by the two methods and those from the Chinese Academy of Sciences (CAS) is shown at levels of 0.15 ns and 0.1 ns, respectively. The results from observations spanning 6 months show that the multi-GNSS OSB estimates for signals in the same frequency bands may have very similar code bias characteristics, and the receiver OSB estimates present larger standard deviations (STDs) than the satellite OSB estimates. Additionally, the variations in the receiver OSB estimates are shown to be related to the types of receivers and antennas and the firmware version. The results also indicate that the root mean square (RMS) of the differences between the OSBs estimated based on the CAS- and German Aerospace Center (DLR)-provided DCB products are 0.32 ns for the global positioning system (GPS), 0.45 ns for the BeiDou navigation satellite system (BDS), 0.39 ns for GLONASS and 0.22 ns for Galileo.

More sources

Dissertations / Theses on the topic "Satellite estimates":

Enbäck, Henrik, and Charlotta Eriksson. "Hybrid Rainfall Estimates from Satellite, Lightning and Ground Station Data in West Africa." Thesis, Uppsala universitet, Institutionen för geovetenskaper, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-254757.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Most of the working population in Ghana are farmers. It is of importance for them to know where and when precipitation will occur to prevent crop losses due to droughts and floodings. In order to have a sustainable agriculture, improved rainfall forecasts are needed. One way to do that is to enhance the initial conditions for the rainfall models. In the mid-latitudes, in-situ rainfall observations and radar data are used to monitor weather and measure rainfall. However, due to the lack of station data and the present absence of a radar network in West Africa, other rainfall estimates are needed as substitutes. The rainfall amount in convective systems, dominating in West Africa, is coupled to their vertical structure. Therefore, satellite measurements of cloud top temperatures and microwave scatter, as well as the number of lightning, can be used to estimate the amount of rainfall. In this report, derived rainfall estimates from satellites and the use of lightning data are analysed to see how well they estimate the actual rainfall amount. The satellite datasets used in this report are NOAA RFE2.0, NOAA ARC2, and the EUMETSAT MPE. The datasets were compared to in-situ measurements from GTS- and NGO collaborating observation stations in order to verify which satellite dataset that best estimates the rainfall or, alternatively, if a combination between two or all the datasets is a better approach. Lightning data from Vaisala GLD360 have been compared to GTS-station data and RFE2.0 to see if a relation between the number of lightning and rainfall amount could be found. It was also tested whether a combination between the satellite- and lightning data could be a better estimate than the two approaches separately. Rainfall estimates from RFE2.0 alone showed the best correlation to GTS- and the NGO collaborating station data. However, a difference in how well RFE2.0 estimated rainfall at GTS-stations compared to reference stations was seen. Comparing RFE2.0 to GTS-stations showed a better correlation, probably due to the use of these observations in the build up of RFE2.0. Even though RFE2.0 showed the best correlation compared to other datasets, satellite estimates showed in general poor skill in catching the actual rainfall amount, strongly underestimating heavy rainfall and somewhat overestimating lighter rainfall. This is probably due to the rather basic assumptions that the cloud top temperature is directly coupled to rain rate and also the poor temporal resolution of the polar orbiting satellites (carrying microwave sensors). Better instruments and algorithms need to be developed to be able to use satellite datasets as an alternative to rainfall measurements in West Africa. Furthermore, due to the lack of station data, only tentative results between GLD360 and GTS-stations could be made, showing a regime dependence. When further analysed to RFE2.0, a stronger temporal dependence, i.e. seasonal variation, rather than a spatial one was seen, especially during the build up of the monsoon. However, due to poor rainfall estimates from RFE2.0, no accurate rainfall-lightning relation could be made but trends regarding the relation were seen. The use of GLD360 showed to be an effective way to erase false precipitation from satellite estimates as well as locating the trajectory of convective cells. To be able to further analyse rainfall/lightning relation, more measurements of the true rainfall is needed from e.g. a radar.
Majoriteten av Ghanas befolkning arbetar inom jordbrukssektorn. Det är viktigt för jordbrukarna att veta när och var nederbörd kommer att falla för att deras skörd inte ska bli förstörd av till exempel torka eller översvämningar. Det behövs därför bättre nederbördsprognoser för ett hållbart jordbruk. Ett sätt att få mer noggranna prognoser är att förbättra initialvärden till nederbördsmodellerna. Vid de mellersta breddgraderna på norra halvklotet används nederbördsmätningar från in-situ stationer samt data från radarsystem som initialvärden, men på grund av få mätstationer och inget radarsystem i västra Afrika behövs alternativa nederbördsestimater. Nederbörden i västra Afrika domineras av konvektiva system, vars regnmängd är kopplad till dess vertikala struktur. Satellitmätningar av molntoppstemperaturen och mikrovågornas spridning och absorption, liksom antalet blixtar är också relaterat till molnets struktur och kan därför användas för att estimera nederbördsmängden. I den här rapporten analyserades nederbördsestimater från satellitdata samt användning av blixtdata för att undersöka hur bra metoderna är på att estimera den verkliga nederbördsmängden. Satellitdataseten som analyserades var NOAA RFE2.0, NOAA ARC2 och EUMETSAT MPE. Dataseten jämfördes med in-situ mätningar från GTS-stationer samt observationerfrån NGO-samarbetande jordbrukare för att verifiera vilket satellitdataset som ger det bästa nederbördsestimatet, alternativt att en kombination mellan två eller alla dataset ger det bästa estimatet. Vidare har blixtdata från Vaisala GLD360 jämförts med GTS-stationer och RFE2.0 för att se om antalet blixtar är relaterat till nederbördsmängden. Slutligen har det också undersökts om en kombination mellan satellit- och blixtdata är ett bättre än de två metoderna separat. Nederbördsestimater från RFE2.0 visade på bäst korrelation med både GTS- och NGO-stationer. En tydlig skillnad noterades dock i RFE2.0:s förmåga att estimera nederbörd vid jämförelse mellan de två stationsdataseten. En bättre korrelation mellan RFE2.0 och GTS-stationerna påvisades, troligen för att RFE2.0 använder dessa observationer i uppbyggnaden av datasetet. Även om RFE2.0 visade på bäst korrelation i jämförelse med ARC2 och MPE var samtliga satellitdataset dåliga på att estimera den verkliga nederbördsmängden. De underestimerar starkt stora mängder nederbörd samtidigt som de överestimerar små mängder. Anledningen är troligen det relativt enkla antagandet att molntoppstemperaturen är direkt kopplad till molnets regnmängd samt den dåliga tidsupplösningen på de polära satelliterna som är utrustade med mikrovågssensorer. För att satellitdataseten ska kunna användas som ett alternativt nederbördsestimat i Västafrika behövs bättre mätinstrument och algoritmer. Vid analysen mellan GLD360 och GTS-stationer kunde, på grund av för få stationsdata, endast övergripande resultat erhållas. Ett områdesberoende gick dock att urskilja som vid en ytterligare analys mellan GLD360 och RFE2.0 visade på ett större säsongsberoende, särskilt under uppbyggnaden av monsunperioden i april och maj. Eftersom RFE2.0 visade sig ha dåliga nederbördsestimat kunde ingen noggrann koppling hittas, utan resultatet visade på trender samt möjligheter att kunna använda blixtdata som ett alternativt nederbördsestimat. Till exempel visade det sig att GLD360 kunde användas som ett verktyg för att sålla bort falsk nederbörd från satellitestimat samt identifiera trajektorien för ett konvektivt system. För en djupare analys i att relatera blixtar och nederbörd i Västafrika krävs bättre tekniker för att estimera nederbörd eller fler in-situ observationer.

Mote, Shekhar Raj. "EVALUATION OF STATE-OF-THE-ART PRECIPITATION ESTIMATES: AN APPROACH TO VALIDATE MULTI-SATELLITE PRECIPITATION ESTIMATES." OpenSIUC, 2018. https://opensiuc.lib.siu.edu/theses/2364.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Availability of precipitation data is very important in every aspect related to hydrology. Readings from the ground stations are reliable and are used in hydrological models to do various analysis. However, the predictions are always associated with uncertainties due to the limited number of ground stations, which requires interpolation of the data. Meanwhile, groundbreaking approach in capturing precipitation events from vantage point through satellites in space has created a platform to not only merge ground data with satellite estimates to produce more accurate result, but also to find the data where ground stations are not available or scarcely available. Nevertheless, the data obtained through these satellite missions needs to be verified on its temporal and spatial resolution as well as the uncertainties associated before we make any decisions on its basis. This study focuses on finding and evaluating data obtained from two multi-satellite precipitation measurements missions: i) Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) ii) Global Precipitation Measurement (GPM) mission. GPM is the latest mission launched on Feb 28, 2014 after the successful completion of TRMM mission which collected valuable data for 17 years since its launch in November 1997. Both near real time and final version precipitation products for TMPA and GPM are considered for this study. Two study areas representing eastern and western parts of the United States of America (USA) are considered: i) Charlotte (CLT) in North Carolina ii) San Francisco (SF) in California. Evaluation is carried out for daily accumulated rainfall estimates and single rainfall events. Statistical analysis and error categorization of daily accumulated rainfall estimates were analyzed in two parts: i) Ten yeas data available for TMPA products were considered for historical analysis ii) Both TMPA and GPM data available for a ten-month common period was considered for GPM Era analysis. To study how well the satellite estimates with their finest temporal and spatial resolution capture single rainfall event and to explore their engineering application potential, an existing model of SF watershed prepared in Infoworks Integrated Catchment Model (ICM) was considered for hydrological simulation. Infoworks ICM is developed and maintained by Wallingford Software in the UK and SF watershed model is owned by San Francisco Public Works (SFPW). The historical analysis of TMPA products suggested overestimation of rainfall in CLT region while underestimation in SF region. This underestimation was largely associated with missed-rainfall events and negative hit events in SF. This inconsistency in estimation was evident in GPM products as well. However, in the study of single rainfall events with higher magnitude of rainfall depth in SF, the total rainfall volume and runoff volume generated in the watershed were over-estimated. Hence, satellite estimates in general tends to miss rainfall events of lower magnitude and over-estimate rainfall events of higher magnitude. From statistical analysis of GPM Era data, it was evident that GPM has been able to correct this inconsistency to some extent where it minimized overestimation in CLT region and minimized negative error due to underestimation in SF. GPM products fairly captured the hydrograph shape of outflow in SF watershed in comparison to TMPA. From this study, it can be concluded that even though GPM precipitation estimates could not quiet completely replace ground rain gage measurements as of now, with the perpetual updating of algorithms to correct its associated error, it holds realistic engineering application potential in the near future.

Smolinski, Steven P. "Marine boundary layer depth and relative humidity estimates using multispectral satellite measurements." Thesis, Monterey, California. Naval Postgraduate School, 1988. http://hdl.handle.net/10945/23069.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

A technique is presented to estimate surface relative humidity and boundary layer depth from multispectral satellite measurements using the AVHRR sensor on TIROS-N generation satellites. A sensitivity study quantifies the effect of a combination of input measurement errors of sea-surface temperature, optical depth and total water vapor used in the technique to produce outputs of surface relative humidity and boundary layer depth under simulated conditions and model atmospheres. Technique verification is then accomplished with satellite data compared to ship and aircraft vertical soundings and sea-surface temperature measurements. The root mean square differences between the surface relative humidity/boundary layer depth satellite-measured estimates and verified measurements are 6% and 75 m respectively. Finally, synoptic-scale mapping of the surface relative humidity and boundary layer depth fields based on the satellite derived estimates is accomplished with monochromatic and color enhanced satellite images. Horizontal variability of surface relative humidity and boundary layer depth on the order of kilometers can be visually detected from these images. Keywords: Remote sensing; MABL(Marine Boundary Layer); Marine atmospheres; Air ocean interface; Advanced very high resolution radiometers; Theses. (edc)

Teo, Chee-Kiat. "Application of satellite-based rainfall estimates to crop yield forecasting in Africa." Thesis, University of Reading, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.434333.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Margulis, Steven A. (Steven Adam) 1973. "Temporal disaggregation of satellite-derived monthly precipitation estimates for use in hydrologic applications." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/17453.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Horvath, Akos. "Differences between satellite measurements and theoretical estimates of global cloud liquid water amounts." Diss., The University of Arizona, 2004. http://hdl.handle.net/10150/280553.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

This dissertation investigated the estimation of global cloud water amounts. The study was prompted by the large discrepancy in published global mean values of cloud liquid water path. Microwave and optical satellite measurements of this quantity range from 25 g/m² to 80 g/m². Theoretical estimates are significantly larger with a current best guess value of 380 g/m². The major limitations of microwave measurements were found to be the inadequate separation of the cloud- and rainwater components, and the lack of retrievals over land. Optical observations were found to be constrained by the truncation of retrieved optical thickness due to saturation effects, the limited knowledge of drop effective radius as a function of optical thickness and rain rate, and plane-parallel retrieval errors due to 3D effects. An analysis of the potential uncertainties concluded that the current theoretical estimate of the global mean cloud liquid water path of 380 g/m² was reasonable with an uncertainty of ±80 g/m². Errors in the optical retrievals due to 3D effects were estimated using a multiangle data set. A microwave-optical comparison revealed that a drop effective radius significantly larger than the common assumption of 8-10 μm was required to remove the low bias of optical retrievals of cloud liquid water in precipitating systems. The low bias due to saturation effects was accounted for by sigmoidal extrapolation of the cumulative distribution of cloud optical thickness. Overall it was found that the optical measurement of the global mean cloud liquid water path could be increased to a maximum of 150 g/m² over the oceans. The failure to close the gap between satellite measurements and theoretical estimates can partly be attributed to, but cannot be completely explained by, the lack of the most intense continental clouds in the ocean-only data set used in this study. It is unlikely that optical measurements can be corrected to accurately retrieve the largest liquid water amounts. New techniques are required to handle the wettest precipitating clouds.

Athey, Ashley Taylor. "Verification of Satellite Derived Precipitation Estimates Over Complex Terrain: A Ground Truth Analysis for Nepal." Thesis, Virginia Tech, 2015. http://hdl.handle.net/10919/52917.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Precipitation estimates from the satellite-based Tropical Rainfall Measuring Mission (TRMM) instrumentation play a key role in flood analysis and water resource management across many regions of the world where rain gauge data are sparsely available. Previous studies have produced conflicting results regarding the accuracy of satellite-derived precipitation products, and several authors have called for further examination of their utility, specifically across the Himalaya Mountains region of southern Asia. In this study, daily precipitation estimates generated by TRMM were compared to daily precipitation measurements from a rain gauge network across the country of Nepal. TRMM data were statistically analyzed to quantify their representation of the gauge data during the four precipitation-defined seasons of Nepal. A detailed case study was assembled for the TRMM grid cell characterized by the greatest precipitation gauge density to develop a deeper understanding of local precipitation variability that the coarse resolution TRMM product cannot capture. The results illustrate that TRMM performs relatively well across all seasons, though the performance of TRMM during frozen precipitation events is not clear. In general, TRMM underestimates daily precipitation during the monsoon and pre-monsoon seasons, and overestimates during the winter and post-monsoon season. The case study analysis revealed a threshold for TRMM bias of 10-20mm of daily precipitation, overestimating lighter precipitation events while underestimating heavier precipitation events. Still, TRMM data compare favorably to gauge data, which contributes to the confidence with which they and other satellite-derived data products are used.
Master of Science

Hyzer, Garrett. "Effects of GPS Error on Animal Home Range Estimates." Scholar Commons, 2012. http://scholarcommons.usf.edu/etd/4338.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

This study examined how variables related to habitat cover types can affect the positional accuracy of Global Positioning System (GPS) data and, subsequently, how wildlife home range analysis can be influenced when utilizing this inaccurate data. This study focused on measuring GPS accuracy relative to five habitat variables: open canopy, sparse canopy, dense canopy, open water, and building proximity. The study took place in Hillsborough County, in residential areas that contain all of these habitat types. Five GPS devices, designed for wildlife tracking purposes, were used to collect the data needed for this study. GPS data was collected under the aforementioned scenarios in order to induce error into the data sets. Each data set was defined as a 1-hour data collecting period, with a fix rate of 60 seconds, which resulted in 60 points per sample. The samples were analyzed to determine the magnitude of effect the five variables have on the positional accuracy of the data. Thirty samples were collected for each of the following scenarios: (1) open grassland with uninhibited canopy closure, (2) sparse vegetation canopy closure, (3) dense vegetation canopy closure, (4) close proximity to buildings (<2 m), and (5) open water with uninhibited canopy closure. Then, GPS errors (in terms of mean and maximum distance from the mean center of each sample) were calculated for each sample using a geographic information system (GIS). Confidence intervals were calculated for each scenario in order to evaluate and compare the levels of error. Finally, this data was used to assess the effect of positional uncertainty on home range estimation through the use of a minimum convex polygon home range estimation technique. Open grassland and open water cover types were found to introduce the least amount of positional uncertainty into the data sets. The sparse coverage cover type introduces a higher degree of error into data sets, while the dense coverage and building proximity cover types introduce the greatest amount of positional uncertainty into the data sets. When used to create minimum convex polygon home range estimates, these data sets show that the home range estimates are significantly larger when the positional error is unaccounted for as opposed to when it is factored into the home range estimate.

Robertson, Noel Arthur. "Model-based and satellite estimates of snow hydrology and carbon fluxes at high latitudes." Thesis, University of Sheffield, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.555106.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

This thesis is a study of how satellite data can be used to improve our understanding of the snow hydrology of boreal regions and its effects on their hydrological and carbon balance. The key parameter is the snow water equivalent (SWE). The thesis addresses two themes; (1) snow parameter retrieval from satellite data, and (2) the dependence on SWE of the hydrological and carbon balance of boreal regions. Using radiative transfer models, linked to a climate-driven snow model, it was found that the brightness temperature is most sensitive to the snow grain size, with SWE the second most significant parameter. Comparison of model predictions with SSMII satellite data showed a significant difference in spatial patterns in central Siberia. The most likely reasons are thought to be either an inaccurate estimate of snow grain size, or errors in the land cover description. For SWE or snow depth retrieval from passive microwave data to be successful, the evolution of the grain size needs to be better quantified. The combination of dynamic algorithm in early winter and static algorithm in middle to late winter produces the best overall results, particularly in Eurasia. The final part of the thesis uses a Dynamic Vegetation Model, SDGVM, to model the hydrological and carbon balance of the major boreal catchments, using a simple climate-driven model of SWE. It was found that there. was broad agreement in the annual water balance between the SDGVM and measured data of river discharge. There is a significant difference in seasonal timing due to the absence of some hydrological processes in SDGVM. Analysis of 21 SI century climate scenarios for the Ob basin indicates a slight reduction in annual runoff, but a significant increase in Net Biome Productivity.

Oliveira, Rômulo Augusto Jucá. "Characteristics and error modeling of GPM satellite rainfall estimates during CHUVA campaign in Brazil." Instituto Nacional de Pesquisas Espaciais (INPE), 2017. http://urlib.net/sid.inpe.br/mtc-m21b/2017/05.22.17.16.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Studies that investigate and evaluate the quality, limitations and uncertainties of satellite rainfall estimates are fundamental to assure the correct and successful use of these products in applications, such as climate studies, hydrological modeling and natural hazard monitoring. Over regions of the globe that lack in situ observations, such studies are only possible through intensive field measurement campaigns, which provide a range of high quality ground measurements, e.g., CHUVA (Cloud processes of tHe main precipitation systems in Brazil: A contribUtion to cloud resolVing modeling and to the GlobAl Precipitation Measurement) and GoAmazon (Observations and Modeling of the Green Ocean Amazon) over the Brazilian Amazon during 2014/2015. This study aims to assess the uncertainty of the Global Precipitation Measurement (GPM) satellite constellation in representing the main characteristics of precipitation over different regions of Brazil. The Integrated Multi-satellitE Retrievals for GPM (IMERG) (level-3) and the Goddard Profiling Algorithm (GPROF) (level-2) algorithms are evaluated against ground-based radar observations, specifically, the S-band weather radar from the Amazon Protection National System (SIPAM) and the X-band dual polarization weather radar (X-band CHUVA radar) as references. The space-based rainfall estimates, based on active microwave (e.g., TRMM-PR and GPM-DPR [at Ku-band] radars) are also used as references. The results for the CHUVA-Vale campaign suggest that GPROF has relatively good agreement (spatial distribution and accumulated rainfall), especially for convective rain cases, due the significant presence of ice scattering. However, the intensity and volume of light/moderate rains is overestimated and performance related to light/heavy rains (underestimated) are intrinsically linked to convectivestratiform rainfall occurrences over the study region. For the study over the Central Amazon Region (CHUVA-GoAmazon), results showed that during the wet season, IMERG, which uses the GPROF2014 rainfall retrieval from the GPM Microwave Imager (GMI) sensor, significantly overestimates the frequency of heavy rainfall volumes at around 00:0004:00 UTC and 15:0018:00 UTC. This overestimation is particularly evident over the Negro, Solimões and Amazon rivers due to the poorlycalibrated algorithm over water surfaces. On the other hand, during the dry season, the IMERG product underestimates mean precipitation in comparison to the S-band SIPAM radar, mainly due to the fact that isolated convective rain cells in the afternoon are not detected by the satellite precipitation algorithm. The study based on verification of GPM level 2 by traditional and object-based analysis shows that volume and occurrence of heavy rainfall are underestimated, a good agreement of GPROF2014 for TMI and GMI versus TRMM PR and GPM DPR (Ku band) rainfall retrievals, respectively, was noted. Such most evident good performances were found through continuous and categorical analyses, especially during the wet season, where the number of objects and larger areas were observed. The larger object area seen by GPROF2014(GMI) compared to DPR (Ku band) was directly linked to the structure of vertical profiles of the precipitanting systems and the presence of bright band was the main source of uncertainty on the estimation of precipitation area and intensity. The results via error modeling, through the Precipitation Uncertainties for Satellite Hydrology (PUSH) framework, demonstrated that the PUSH model was suitable for characterizing the error from the IMERG algorithm when applied to S-band SIPAM radar estimates. PUSH could efficiently predict the error distribution in terms of spatial and intensity distributions. However, an underestimation (overestimation) of light satellite rain rates was observed during the dry (wet) period, mainly over the river. Although the estimated error showed a lower standard deviation than the observed error, they exhibited good correlations to other, especially in capturing the systematic error along the Negro, Solimões and Amazon rivers, especially during the wet season.
Estudos que investigam e avaliam a qualidade, limitações e incertezas das estimativas de precipitação de satélites são fundamentais para assegurar o uso correto e bem-sucedido desses produtos em aplicações, como estudos climáticos, modelagem hidrológica e monitoramento de desastres naturais. Em regiões do globo que não possuem observações in situ, esses estudos apenas são possíveis através de campanhas intensivas de medição de campo, que oferecem uma gama de medições de superfície de alta qualidade, por exemplo, CHUVA (Cloudprocesses of tHe main precipitation systems in Brazil: A contribUtion to cloud re-solVing modeling and to the GlobAl Precipitation Measurement) e GoAmazon (Observations and Modeling of the Green Ocean Amazon) sobre a Amazônia Brasileira durante 2014/2015. Este estudo tem como objetivo avaliar as incertezas provenientes da constelação de satélites do Global Precipitation Measurement (GPM) em representar as principais características da precipitação em diferentes regiões do Brasil. Os algoritmos Integrated Multi-satellitE Retrievals for GPM (IMERG) (level-3) e Goddard Profiling Algorithm (GPROF) (level-2) são avaliados em contraste as observações de radares meteorológicos, especificamente, do Sistema Nacional de Proteção da Amazônia (SIPAM) e o radar meteorológico banda X de dupla polarização (X-band CHUVA radar) como referência. As estimativas de precipitação, baseadas em radares de microondas ativos (por exemplo, radares TRMM-PR e GPM-DPR [na banda Ku]) também são utilizadas como referência. Os resultados da campanha CHUVA-Vale sugerem que o GPROF possui uma boa concordância (distribuição espacial e precipitação acumulada), especialmente para casos de chuva convectiva, devido à presença significativa de espalhamento por gelo. No entanto, a intensidade e volume de chuvas leves/moderadas é superestimada e um desempenho (subestimado) relacionado às chuvas fracas/intensas diretamente ligado às ocorrências de chuvas convectivasestratiformes na região do estudo. Para o estudo da região da Amazônia Central (CHUVA-GoAmazon), os resultados mostraram que, durante a estação chuvosa, o IMERG, que utiliza as estimativas de precipitação do GPROF2014 a partir do sensor GPM Microwave Imager (GMI), superestima significativamente a freqüência de chuvas intensas em torno de 00:00-04:00 UTC e 15:00-18:00 UTC. Essa superestimativa é particularmente evidente nos rios Negro, Solimões e Amazonas devido ao algoritmo apresentasse erroneamente calibrado sobre as superfícies de água. Por outro lado, durante a estação seca, o produto IMERG subestima a precipitação média em comparação com o radar banda-s do SIPAM, principalmente devido ao fato de que células convectivas isoladas à tarde não são detectadas por tal algoritmo. O estudo baseado na verificação das estimativas do GPM Level 2 por abordagens tradicional e baseada em objeto mostra que, embora a subestimiativa do volume e ocorrência de chuvas intensas, foi observada uma boa concordância do GPROF2014 (TMI e GMI) versus TRMM PR e GPM DPR (Ku band), Respectivamente. Tais evidentes melhores desempenhos foram encontrados através de análises contínua e categórica, especialmente durante a estação chuvosa, onde o maior número e maiores áreas de objetos foram observados. As maiores áreas, observadas pelo GPROF2014 (GMI) comparada ao DPR (banda Ku) esteve diretamente ligada à estrutura de perfis verticais dos sistemas de precipitantes e a presença de banda brilhante foi a principal fonte de incerteza na estimativa da área e intensidade de precipitação. Os resultados referentes à modelagem do erro, através da ferramenta Precipitation Uncertainties for Satellite Hydrology (PUSH), as análises demonstraram que o modelo PUSH foi adequado para caracterizar o erro do algoritmo IMERG quando aplicado às estimativas de radar banda S do SIPAM. O modelo PUSH pôde prever eficientemente a distribuição de erro em termos espaciais e de intensidade. No entanto, observou-se uma subestimativa (superestimativa) das taxas de chuva fracas do satélite durante o período seco (chuvoso), especialmente ao longo do rio. Embora o erro estimado tenha apresentado menor desvio padrão do que o erro observado, eles apresentaram boas correlações entre si, especialmente na captura do erro sistemático ao longo dos rios Negro, Solimões e Amazonas, especialmente durante a estação chuvosa.

More sources

Books on the topic "Satellite estimates":

United States. National Aeronautics and Space Administration., ed. Reusable Reentry Satellite (RRS): System cost estimates document. Torrance, Calif: Science Applications International Corp., 1991.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

L, Colborn B., Science Applications International Corporation, and George C. Marshall Space Flight Center. Astrophysics Division., eds. Scoping estimates of the LDEF satellite induced radioactivity. Prospect, Tenn: Science Applications International Corporation, 1990.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

Fortune, Michael A. Automated satellite-based estimates of precipitation: An assessment of accuracy. Washington, D.C: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Environmental Satellite, Data, and Information Service, 1998.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

Find full text

APA, Harvard, Vancouver, ISO, and other styles

Find full text

APA, Harvard, Vancouver, ISO, and other styles

Find full text

APA, Harvard, Vancouver, ISO, and other styles

United States. National Environmental Satellite, Data, and Information Service., ed. Automated satellite-based estimates of precipitation: An assessment of accuracy. Washington, D.C: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Environmental Satellite, Data, and Information Service, 1998.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

Find full text

APA, Harvard, Vancouver, ISO, and other styles

Tai, Chang-Kou. On estimating the basin-scale ocean circulation from satellite altimetry. La Jolla, CA: Scripps Institution of Oceanography, 1988.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

Tai, Chang-Kou. On estimating the basin-scale ocean circulation from satellite altimetry. La Jolla, CA: Scripps Institution of Oceanography, 1988.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

More sources

Book chapters on the topic "Satellite estimates":

Ebert, Elizabeth E. "Methods for Verifying Satellite Precipitation Estimates." In Measuring Precipitation From Space, 345–56. Dordrecht: Springer Netherlands, 2007. http://dx.doi.org/10.1007/978-1-4020-5835-6_27.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Coifman, R., and S. Semmes. "L 2 Estimates in Nonlinear Fourier Analysis." In ICM-90 Satellite Conference Proceedings, 79–95. Tokyo: Springer Japan, 1991. http://dx.doi.org/10.1007/978-4-431-68168-7_7.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Antoñanzas-Torres, F., J. Antonanzas, F. J. Martínez-de-Pisón, M. Alia-Martinez, and O. Perpiñán-Lamigueiro. "Downscaling of Solar Irradiation from Satellite Estimates." In Lecture Notes in Management and Industrial Engineering, 197–205. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-12754-5_15.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Martin, Timothy C., Richard G. Allen, Larry E. Brazil, J. Philip Burkhalter, and Jason S. Polly. "Evapotranspiration Estimates from Remote Sensing for Irrigation Water Management." In Satellite-based Applications on Climate Change, 195–216. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-5872-8_13.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Carbery, Anthony, Eugenio Hernández, and Fernando Soria. "Estimates for the Kakeya Maximal Operator on Radial Functions in Rn." In ICM-90 Satellite Conference Proceedings, 41–50. Tokyo: Springer Japan, 1991. http://dx.doi.org/10.1007/978-4-431-68168-7_4.

Full text

APA, Harvard, Vancouver, ISO, and other styles

McConnell, Alan, and Gerald R. North. "Sampling Errors in Satellite Estimates of Tropical Rain." In Collected Reprint Series, 9567–70. Washington, DC: American Geophysical Union, 2013. http://dx.doi.org/10.1002/9781118782071.ch3.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Tarnavsky, Elena, and Rogerio Bonifacio. "Drought Risk Management Using Satellite-Based Rainfall Estimates." In Advances in Global Change Research, 1029–53. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-35798-6_28.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Antolini, Fabrizio, Antonio Giusti, and Francesca Petrei. "Tourism and territorial economy: beyond satellite accounting." In Proceedings e report, 71–76. Florence: Firenze University Press and Genova University Press, 2023. http://dx.doi.org/10.36253/979-12-215-0106-3.13.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

The tourism sector can be an important factor for the economic development. The increase in the present population, due to tourist flows, also activates a series of other sectors, generating significant economic benefits. At international level, the main tool for this type of evaluation has been identified in satellite accounting, which estimates the value added of the tourism sector, possibly increased by that coming from other similar sectors, obtaining the tourism value added. At European level, satellite accounting is a voluntary exercise for countries, which almost never finds a dimension of territorial detail, limiting itself to the national level. Having an accounting representation at a territorial level, even if only regional, would instead be very useful for both descriptive and forecasting purposes (Input-Output). In order to reproduce the estimates of satellite accounting at a territorial level, it is important to have monetary (tourist expenses) and non-monetary (attendance) variables. For Italy, if counted from the offer side rather than the demand side, these variables are available with at least regional territorial detail. On the other hand, the temporal alignment of this information is different, since tourist spending and tourist presences come from different surveys. However, we could start developing a methodology that starts from the offer, using value added as an accounting approach. The advantages would be important: the statistical information used would in fact be that contained in the Business Register FRAME, with considerable advantages from the point of view of the timeliness and homogeneity of the statistical data. Naturally, the various economic activities must be weighed for their link with tourism sector. This source of information could enjoy also the possibility of having information deriving from electronic invoicing. In this work, an attempt to estimate the tourism value added through the offer side is experimented at the regional level.

Field, Robert D. "Using Satellite Estimates of Precipitation for Fire Danger Rating." In Advances in Global Change Research, 1131–54. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-35798-6_33.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Turk, F. Joseph, and Amita V. Mehta. "Toward Improvements in Short-time Scale Satellite-Derived Precipitation Estimates using Blended Satellite Techniques." In Measuring Precipitation From Space, 281–90. Dordrecht: Springer Netherlands, 2007. http://dx.doi.org/10.1007/978-1-4020-5835-6_22.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Satellite estimates":

Von Arnim, Maximilian, Steffen Gaisser, and Sabine Klinkner. "Improved sensor fusion for flying laptop based on a multiplicative EKF." In Symposium on Space Educational Activities (SSAE). Universitat Politècnica de Catalunya, 2022. http://dx.doi.org/10.5821/conference-9788419184405.049.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Flying Laptop is a small satellite carrying an optical communications payload. It was launched in 2017. To improve the satellite’s attitude determination, which is used to point the payload, a new sensor fusion algorithm based on a low pass filter and a multiplicative extended Kalman filter (MEKF) was developed. As an operational satellite, improvements are only possible via software updates. The algorithm estimates the satellite's attitude from star tracker and fibre-optical gyroscope (FOG) measurements. It also estimates the gyroscope bias. The global attitude estimate uses a quaternion representation, while the Kalman filter uses Gibbs Parameters to calculate small attitude errors. Past Kalman filter predictions are saved for several time steps so that a delayed star tracker measurement can be used to update the prediction at the time of measurement. The estimate at the current time is then calculated by predicting the system attitude based on the updated past estimate. The prediction step relies on the low-pass-filtered gyroscope measurements corrected by the bias estimate. The new algorithm was developed as part of a master’s thesis at the University of Stuttgart, where Flying Laptop was developed and built. It was simulated in a MATLAB/Simulink environment using the European Space Agency’s GAFE framework. In addition, the new filter was applied to measurement data from the satellite. The results were used to compare the performance with the current filter implementation. The new Kalman filter can deal with delayed, missing, or irregular star tracker measurements. It features a lower computational complexity than the previous standard extended Kalman filter used on Flying Laptop. The mean error of the attitude estimate was reduced by up to 90%. The low pass filter improves the rotation rate estimate between star tracker measurements, especially for biased and noisy gyroscopes. However, this comes at the cost of potentially less accurate attitude estimates. Educational satellites benefit from the new algorithm given their typically limited processing power and cheap commercial-off-the-shelf (COTS) sensors. This paper presents the approach in detail and shows its benefits

Rosin, Paul L. "Refining region estimates for post-processing image classification." In Satellite Remote Sensing, edited by Jacky Desachy. SPIE, 1994. http://dx.doi.org/10.1117/12.196718.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Bian, Jeffrey Y., Juliana Y. Leung, Nick Volkmer, and Jingwen Zheng. "An Improved Workflow in Mass Balance Approach for Estimating Regional Methane Emission Rate Using Satellite Measurements." In SPE Canadian Energy Technology Conference and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/212791-ms.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Abstract Satellite-retrieved methane (CH4) concentration data offers a valuable opportunity for large-scale emissions monitoring. However, its widespread adoption remains challenging due to the data volume and varying data quality. A workflow to estimate the methane emission rate of major hydrocarbon plays based on the mass balance principle using publicly available Sentinel-5P satellite data is presented. This workflow estimates the methane emission rate originating from specific regions. The proposed workflow is applied to estimate emissions from the Permian Basin and the Appalachian Basin in the United States. The results are compared against volumes estimated by other means and reported in the literature. The proposed method is easy to implement and offers promising potential for practical and reliable estimates for long-term regional methane emission monitoring purposes.

Habte, Aron, Manajit Sengupta, and Stephen Wilcox. "Comparing Measured and Satellite-Derived Surface Irradiance." In ASME 2012 6th International Conference on Energy Sustainability collocated with the ASME 2012 10th International Conference on Fuel Cell Science, Engineering and Technology. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/es2012-91417.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

The purpose of this study is two-fold: 1) To examine the performance of the Global Solar Insolation Project (GSIP) physics-based model in characterizing global horizontal solar radiation across the United States by comparing to the ground measured data, and 2) to examine improvements of the GSIP data to address temporal and spatial variations. The study enumerates and examines the spatial and temporal limitations of the GSIP model. Most comparisons demonstrate relatively good statistical agreement. However, the methodology used in the satellite model to distinguish microclimate conditions presents significant challenges, and the model requires refinement in addressing aerosol estimates, water vapor estimates, and clear sky optical properties. Satellite derived datasets are only available at half-hour intervals. Surface measurement can easily be made at temporal resolution in the order of seconds. Therefore intra-hour variability, an important quantity for understanding how power production in power plants will vary, cannot be directly derived from satellites. This paper illustrates how intra-hour variability in ground measurements cannot be captured by the satellite based datasets. We also discuss the potential for improved next-generation geostationary satellite data to improve the accuracy of surface radiation estimates.

Painter, Thomas H., Dar A. Roberts, Robert O. Green, and Jeff Dozier. "Improving alpine region spectral mixture analysis estimates of snow-covered area." In Satellite Remote Sensing II, edited by Edwin T. Engman, Gerard Guyot, and Carlo M. Marino. SPIE, 1995. http://dx.doi.org/10.1117/12.227196.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Pini, Agnese, Giovanni Leuzzi, and Paolo Monti. "Estimates of turbulence parameters from satellite-tracked drifters." In 2016 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems (EESMS). IEEE, 2016. http://dx.doi.org/10.1109/eesms.2016.7504839.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Lo Conti, Francesco, Antonia Incontrera, and Leonardo V. Noto. "A local post-retrieval tool for satellite precipitation estimates." In SPIE Remote Sensing, edited by Christopher M. U. Neale and Antonino Maltese. SPIE, 2012. http://dx.doi.org/10.1117/12.974675.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Kuligowski, Robert J. "Satellite rainfall estimates for global flood monitoring and prediction." In Asia-Pacific Remote Sensing Symposium, edited by Felix Kogan, Shahid Habib, V. S. Hegde, and Masashi Matsuoka. SPIE, 2006. http://dx.doi.org/10.1117/12.694170.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Cordero, Lina, Nabin Malakar, Yonghua Wu, Barry Gross, Fred Moshary, and Mike Ku. "Assessing satellite based PM2.5 estimates against CMAQ model forecasts." In SPIE Remote Sensing, edited by Adolfo Comeron, Evgueni I. Kassianov, Klaus Schäfer, Karin Stein, and John D. Gonglewski. SPIE, 2013. http://dx.doi.org/10.1117/12.2029320.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Ioffe, A. D. "On stability estimates for the regularity property of maps." In Proceedings of the ICM 2002 Satellite Conference on Nonlinear Functional Analysis. WORLD SCIENTIFIC, 2003. http://dx.doi.org/10.1142/9789812704283_0014.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Satellite estimates":

Fowlie, Meredith, Edward Rubin, and Reed Walker. Bringing Satellite-Based Air Quality Estimates Down to Earth. Cambridge, MA: National Bureau of Economic Research, February 2019. http://dx.doi.org/10.3386/w25560.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Sengupta, Manajit, and Peter Gotseff. Evaluation of Clear Sky Models for Satellite-Based Irradiance Estimates. Office of Scientific and Technical Information (OSTI), December 2013. http://dx.doi.org/10.2172/1118101.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Hofer, Martin, Tomas Sako, Arturo Martinez Jr., Mildred Addawe, Joseph Bulan, Ron Lester Durante, and Marymell Martillan. Applying Artificial Intelligence on Satellite Imagery to Compile Granular Poverty Statistics. Asian Development Bank, December 2020. http://dx.doi.org/10.22617/wps200432-2.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

This study outlines a computational framework to enhance the spatial granularity of government-published poverty estimates, citing data from the Philippines and Thailand. Computer vision techniques were applied on publicly available medium resolution satellite imagery, household surveys, and census data from the two countries. The results suggest that even using publicly accessible satellite imagery, predictions generally aligned with the distributional structure of government-published poverty estimates after calibration. The study further examines the robustness of the resulting estimates to user-specified algorithmic parameters and model specifications.

Calafat, Francisco Mir, Thomas Frederikse, and Kevin Horsburgh. Mediterranean trend and acceleration sea-level estimates. EuroSea, 2023. http://dx.doi.org/10.3289/eurosea_d5.2_v2.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Sea-level change is geographically non-uniform, with regional departures that can reach several times the global average rate of change. Characterizing this spatial variability and understanding its causes is crucial to the design of adaptation strategies for sea-level rise. This, as it turns out, is no easy feat, primarily due to the sparseness of the observational sea-level record in time and space. Long tide gauge records are restricted to a few locations along the coast. Satellite altimetry offers a better spatial coverage but only since 1992. In the Mediterranean Sea, the tide gauge network is heavily biased towards the European shorelines, with only one record with at least 35 years of data on the African coasts. Past studies have attempted to address the difficulties related to this data sparseness in the Mediterranean Sea by combining the available tide gauge records with satellite altimetry observations. The vast majority of such studies represent sea level through a combination of altimetry-derived empirical orthogonal functions whose temporal amplitudes are then inferred from the tide gauge data. Such methods, however, have tremendous difficulty in separating trends and variability, make no distinction between relative and geocentric sea level, and tell us nothing about the causes of sea level changes. Here, we combine observational data from tide gauges and altimetry with sea-level fingerprints of land-mass changes using a Bayesian hierarchical model (BHM) to quantify the sources of sea-level changes since 1960 in the Mediterranean Sea. The Bayesian estimates are provided on 1/4o x 1/4o regular grid. We find that Mediterranean Sea level rose at a relatively low rate from 1960 to 1990, at which point it started rising significantly faster with comparable contributions from sterodynamic sea level (ocean dynamics and thermal expansion) and land-mass changes. (EuroSea Deliverable, D5.2_v2)

Cogan, James. Some Potential Errors in Satellite Wind Estimates Using the Geostrophic Approximation and the Thermal Wind. Fort Belvoir, VA: Defense Technical Information Center, June 1993. http://dx.doi.org/10.21236/ada269784.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Komppula, Birgitta, Tomi Karppinen, Henrik Virta, Anu-Maija Sundström, Iolanda Ialongo, Kaisa Korpi, Pia Anttila, Jatta Salmi, Johanna Tamminen, and Katja Lovén. Air quality in Finland according to air quality measurements and satellite observations. Finnish Meteorological Institute, September 2021. http://dx.doi.org/10.35614/isbn.9789523361409.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

In this report the current air quality in Finland has been assessed with air quality measurement data and satellite observations. The assessment of ambient air concentrations included following air impurities: NO2, NOx, PM10, PM2,5, SO2, CO, O3, benzo(a)pyrene, benzene, Pb, As, Cd ja Ni. For these pollutants air quality assessment thresholds are given in air quality legislation (2008/50/EY, 2004/107/EY). Assessment has been performed for air quality zones. The main data set included air quality measurements performed in Finland during 2015–2019. Satellite observations were used as an objective assessment tool in analysis of the spatial variation of NO2 and CO concentrations. Air quality measurements show that air quality has improved in Finland in many respects. Especially the need to monitor NO2 and PM10 with continuous measurements has decreased. Growing understanding of national benzo(a)pyrene concentrations has increased the monitoring needs. Efforts to decrease ozone levels still requires international actions. SO2, CO, benzene and heavy metal concentrations are on a low level in Finland outside industrial areas and other assessment methods than continuous monitoring can be used, and the number of continuous monitoring sites has already decreased. Satellite-based concentrations of nitrogen dioxide and carbon monoxide as well as their spatial variation in Finland were analyzed using observations from the TROPOsperic Monitoring Instrument (TROPOMI). The analysis of CO over Finland was carried out for the first time in this project. Results show that overall annual CO concentrations over Finland are low and spatial variability is small. Also, NO2 concentrations over Finland are rather low, but spatial patterns are more clearly visible. The highest NO2 concentrations are observed over the largest cities. By establishing a relationship between ground-based and satellite total column concentrations, surface concentrations of NO2 and CO were estimated from the satellite data for the zones. The satellite-based estimate for annual NO2 surface concentration over Helsinki metropolitan area is 28 μg/m3, and for the rest of Finland mostly between 10–15 μg/m3. For CO the differences between monitoring areas are small, with estimates varying between 160–164 μg/m3 or in other words about 0,16 mg/m3.

Kirkham, Randy R. Comparison of surface energy fluxes with satellite-derived surface energy flux estimates from a shrub-steppe. Office of Scientific and Technical Information (OSTI), December 1993. http://dx.doi.org/10.2172/10135371.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Shrestha, M. S., R. Rajbhandari, and S. R. Bajracharya. Validation of NOAA CPC_RFE2.0 Satellite-based Rainfall Estimates in the Central Himalayas; Working Paper 2013/5. Kathmandu, Nepal: International Centre for Integrated Mountain Development (ICIMOD), 2013. http://dx.doi.org/10.53055/icimod.585.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Full text

APA, Harvard, Vancouver, ISO, and other styles

Sherman, Luke, Jonathan Proctor, Hannah Druckenmiller, Heriberto Tapia, and Solomon Hsiang. Global High-Resolution Estimates of the United Nations Human Development Index Using Satellite Imagery and Machine-learning. Cambridge, MA: National Bureau of Economic Research, March 2023. http://dx.doi.org/10.3386/w31044.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Academic literature on the topic 'Satellite estimates'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Contents

Journal articles on the topic "Satellite estimates":

Dissertations / Theses on the topic "Satellite estimates":

Books on the topic "Satellite estimates":

Book chapters on the topic "Satellite estimates":

Conference papers on the topic "Satellite estimates":

Reports on the topic "Satellite estimates":