National Oceanic And Atmospheric Administration Satellite Research Articles

Retrieval of Outgoing Longwave Radiation from the Fengyun-3D Satellite and Its Climate Applications

The Fengyun-3D (FY-3D) satellite is a Chinese Earth observation satellite with high spectral resolution that can provide multi-spectral observations under all weather conditions. Outgoing longwave radiation (OLR) is an important parameter in the earth radiation energy balance and can reflect changes in atmospheric circulation and convective activity in response to incoming solar radiation. To apply the OLR data of the FY-3D satellite (F_OLR) to weather and climate analyses, the traditional single-channel OLR inversion algorithm for the NOAA (National Oceanic and Atmospheric Administration) satellite was used to calculate F_OLR, and the difference between F_OLR and the OLR data of the NOAA 18 satellite (N_OLR) was analyzed. A correction algorithm was proposed to correct F_OLR to match N_OLR; the spatiotemporal consistency of the corrected F_OLR and N_OLR was evaluated, and the two types of OLR data were used to analyze the onset of the South China Sea Summer Monsoon (SCSSM) and typhoon precipitation in China. The results showed that the corrected F_OLR and N_OLR were consistent in both temporal variation and spatial distribution and that the monitoring of the SCSSM and typhoon precipitation by the two types of OLR data was also in agreement, showing their equivalent quality. Finally, the N_OLR (2006–2019) and the corrected F_OLR (2020-present) were combined to form a long time series OLR dataset that was used in the Beijing Climate Center climate monitoring system in China to monitor abnormal changes in the global convective activity. This study can provide a reference method for future weather and climate applications of Chinese satellites.

Evaluation of the in situ Sea Surface Temperature Quality Control in the NOAA in situ SST Quality Monitor (iQuam) System

AbstractIn situ sea surface temperature (SST) measurements play a critical role in the calibration/validation (Cal/Val) of satellite SST retrievals and ocean data assimilation. However, their quality is not always optimal, and proper quality control (QC) is required before they can be used with confidence. The in situ SST Quality Monitor (iQuam) system was established at the National Oceanic and Atmospheric Administration (NOAA) in 2009, initially to support the Cal/Val of NOAA satellite SST products. It collects in situ SST data from multiple sources, performs uniform QC, monitors the QC’ed data online, and distributes it to users. In this study, the iQuam QC is compared with other QC methods available in some of the in situ data ingested in iQuam. Overall, the iQuam QC performs well on daily-to-monthly time scales over most global oceans and under a wide variety of environmental conditions. However, it may be less accurate in the daytime a when pronounced diurnal cycle is present, and in dynamic regions, due to the strong reliance on the “reference SST check”, which employs daily low-resolution level 4 (L4) analyses with no diurnal cycle resolved. The iQuam “performance history check”, applied to all in situ platforms, is an effective alternative to the customary “black/gray” lists, available only for some platforms (e.g., drifters and Argo floats). In the future, iQuam QC will be upgraded (e.g., using improved reference field(s), with enhanced temporal and spatial resolutions). More comparisons with external QC methods will be performed to learn and employ the best QC practices.

Monitoring of aerosols and studying its effects on the environment and humans health in Iran.

Natural hazards affect different parts of living organisms. One of these hazards is aerosols. Addressing this issue in areas that suffer from this hazard is of critical importance. Scientific research over the past two decades has shown that aerosol particles are one of the main pollutants from the perspective of public health and health. The purpose of the present study is to monitor and model aerosol temporal and spatial variations in Iran. Aerosols or airborne particles with health effects such as heart, vascular and respiratory diseases are associated. For this purpose, MODIS (or Moderate Resolution Imaging Spectroradiometer) and NOAA (or National Oceanic and Atmospheric Administration) satellite data and BTD (or Brightness Temperature Difference) were used. Given the 20-year study period (2000-2019), the output of much satellite data was divided into four five-year periods to monitor aerosols with high accuracy. The results showed that aerosol values in terms of intensity and frequency of optical depth (AOD) increased over the 20-year time series, and its intensity was higher in the last 5years. According to the results obtained from the NODIS and NOAA satellite data and comparing their outputs, NOAA satellite data were associated with outliers, which was significantly different from the other cohort data. For this reason, the MODIS satellite image output was used to monitor aerosol images. The innovation of the present study is the use of remote sensing science to monitor the effects of aerosols on the environment and human's health. According to the results from MODIS satellite data, the maximum optical depth (AOD) of the aerosols is for July 2003 with a value of 0.63, but according to the NOAA satellite data output, the maximum optical depth (AOD) of the aerosols is for March 2013 with a value of 2.54. As the values of aerosols increase in frequency and intensity and the areas with the highest intensity of aerosols have been identified, it can be overcome by careful planning of their problems. Areas, where the amount and volume of aerosols were higher, should be observed in health protocols. By preventing and maintaining good hygiene, the negative effects of aerosols can be reduced.

Developing Priority Observational Requirements from Space Using Multi-Attribute Utility Theory

AbstractOver a two-year period beginning in 2015, a panel of subject matter experts, the Space Platform Requirements Working Group (SPRWG), carried out an analysis and prioritization of different space-based observations supporting the National Oceanic and Atmospheric Administration (NOAA)’s operational services in the areas of weather, oceans, and space weather. NOAA leadership used the SPRWG analysis of space-based observational priorities in different mission areas, among other inputs, to inform the Multi-Attribute Utility Theory (MAUT)-based value model and the NOAA Satellite Observing Systems Architecture (NSOSA) study. The goal of the NSOSA study is to develop candidate satellite architectures for the era beginning in approximately 2030. The SPRWG analysis included a prioritized list of observational objectives together with the quantitative attributes of each objective at three levels of performance: a threshold level of minimal utility, an intermediate level that the community expects by 2030, and a maximum effective level, a level for which further improvements would not be cost effective. This process is believed to be unprecedented in the analysis of long-range plans for providing observations from space. This paper describes the process for developing the prioritized objectives and their attributes and how they were combined in the Environmental Data Record (EDR) Value Model (EVM). The EVM helped inform NOAA’s assessment of many potential architectures for its future observing system within the NSOSA study. However, neither the SPRWG nor its report represents official NOAA policy positions or decisions, and the responsibility for selecting and implementing the final architecture rests solely with NOAA senior leadership.

Satellite agroecological monitoring within the system of sustainable environmental management

Satellite data are a relevant part of information, required for sustainable environmental management, assessment of the impact of economic activity of ecosystems, determination of risks, related to global climate changes, desertifi cation processes, loss of landscape and biotic diversity. Aim. To substantiate the reasonability and prove the effi ciency of using satellite data in the agroecologic monitoring system regarding the impact of climate changes on vegetation, processes of soil erosion degradation, and assessment of landscape diversity. Methods. The study was conducted in the territory of Ukraine. It involved the application of SWOT and Gap-analysis methodology, materials of NOAA satellite observa- tions, Sentinel, different spatial resolution, methodological and regulatory provision of the Institute of Agroecology and Environmental Economics of the National Academy of Agrarian Sciences regarding satellite monitoring of the structure of agrolandscapes, norms of establishing a network of testing agrarian grounds, list of vegetation state indicators, in par- ticular, “Remote sensing of the Earth from space. Land data about controlling the condition of plantings and performance of agricultural crops. General requirements: DSTU 7307:2013”, “Remote sensing of the Earth from space. Ground in- spection of plantings. Classifi er of objects and functions: SUC 01.1-37-907:2011”, “Methodological recommendations on establishing the network of testing agrarian grounds in the system of monitoring of plantings using the materials of cosmic information”. The investigation on the impact of climate changes on vegetation state was conducted on the territory of three natural-climatic zones which were geographically represented by Chernihiv, Poltava and Zaporizhzhia regions re- spectively. The determination of the threat of erosion degradation of arable lands and landscape diversity was performed on the territory of two administrative districts with high level of ploughness of agrolandscapes, intense agrarian produc- tion and manifestation of erosion degradation of lands. Results. Inadequacy of the traditional system of agroecological monitoring was determined. It was proven that it was reasonable to have comprehensive application of satellite data regarding climate warming within the natural climatic zones and its impact on vegetation according to the normalized dif- ference vegetation index (NDVI), erosion degradation of soils and landscape diversity. According to satellite data of the National Oceanic and Atmospheric Administration (NOAA), the correlation analysis was performed on the connection between the dynamics of the sum of effective temperatures and the sum of NDVI values for the vegetation period. There was positive impact of climate warming on vegetation state according to NDVI index in the zone of Polissia and Forest- Steppe. The correlation coeffi cients were R = 0.64 and R = 0.77 respectively. In the Steppe zone the correlation coeffi cient dropped down to R = 0.35 which demonstrated the elevated risk of droughts. Conclusions. Satellite data of Sentinel-1 were used to determine critical zones of erosion degradation of arable lands, requiring preservation and their inclusion to the natural fi elds, which had a positive impact on the optimization of agrolandscape diversity.

On the interpretation of upper-tropospheric humidity based on a second-order retrieval from infrared radiances

Abstract. We present a novel retrieval for upper-tropospheric humidity (UTH) from High-resolution Infrared Radiation Sounder (HIRS) channel 12 radiances that successfully bridges the wavelength change from 6.7 to 6.5 µm that occurred from HIRS/2 on National Oceanic and Atmospheric Administration satellite NOAA-14 to HIRS/3 on satellite NOAA-15. The jump in average brightness temperature (in the water vapour channel; T12) that this change had caused (about −7 K) could be fixed with a statistical inter-calibration method (Shi and Bates, 2011). Unfortunately, the retrieval of UTHi (upper-tropospheric humidity with respect to ice) based on the inter-calibrated data was not satisfying at the high tail of the distribution of UTHi. Attempts to construct a better inter-calibration in the low T12 range (equivalent to the high UTHi range) were either not successful (Gierens et al., 2018) or required additional statistically determined corrections to the measured brightness temperatures (Gierens and Eleftheratos, 2017). The new method presented here is based on the original one (Soden and Bretherton, 1993; Stephens et al., 1996; Jackson and Bates, 2001), but it extends linearisations in the formulation of water vapour saturation pressure and in the temperature dependence of the Planck function to second order. To achieve the second-order formulation we derive the retrieval from the beginning, and we find that the most influential ingredient is the use of different optical constants for the two involved channel wavelengths (6.7 and 6.5 µm). The result of adapting the optical constant is an almost perfect match between UTH data measured by HIRS/2 on NOAA-14 and HIRS/3 on NOAA-15 on 1004 common days of operation. The method is applied to both UTH and UTHi. For each case retrieval coefficients are derived. We present a number of test applications, e.g. on computed brightness temperatures based on high-resolution radiosonde profiles, on the brightness temperatures measured by the satellites on the mentioned 1004 common days of operation. Further, we present time series of the occurrence frequency of high UTHi cases, and we show the overall probability distribution of UTHi. The two latter applications expose indications of moistening of the upper troposphere over the last 35 years. Finally, we discuss the significance of UTH. We state that UTH algorithms cannot be judged for their correctness or incorrectness, since there is no true UTH. Instead, UTH algorithms should fulfill a number of usefulness postulates, which we suggest and discuss.

Drifting Effects of NOAA Satellites on Long-Term Active Fire Records of Europe

Explicit knowledge of different error sources in long-term climate records from space is required to understand and mitigate their impacts on resulting time series. Imagery of the heritage Advanced Very High Resolution Radiometer (AVHRR) provides unique potential for climate research dating back to the 1980s, flying onboard a series of successive National Oceanic and Atmospheric Administration (NOAA) and Meteorological Operational (MetOp) satellites. However, the NOAA satellites are affected by severe orbital drift that results in spurious trends in time series. We identified the impact and extent of the orbital drift in 1 km AVHRR long-term active fire data. This record contains data of European fire activity from 1985–2016 and was analyzed on a regional scale and extended across Europe. Inconsistent sampling of the diurnal active fire cycle due to orbital drift with a maximum delay of ∼5 h over NOAA-14 lifetime revealed a ∼90% decline in the number of observed fires. However, interregional results were less conclusive and other error sources as well as interannual variability were more pronounced. Solar illumination, measured by the sun zenith angle (SZA), related changes in background temperatures were significant for all regions and afternoon satellites with major changes in −0.03 to −0.09 K deg − 1 for ▵ B T 34 (p ≤ 0 . 001). Based on example scenes, we simulated the influence of changing temperatures related to changes in the SZA on the detection of active fires. These simulations showed a profound influence of the active fire detection capabilities dependent on biome and land cover characteristics. The strong decrease in the relative changes in the apparent number of active fires calculated over the satellites lifetime highlights that a correction of the orbital drift effect is essential even over short time periods.

A martingale-based temporal analysis of pre-earthquake anomalies at Jiuzhaigou, China, in the period of 2009-2018

Many studies on the relationship between outgoing longwave radiation (OLR) data observed by National Oceanic and Atmospheric Administration Satellites (NOAA) with strong earthquakes are based on a certain zone or a couple of earthquakes. But it’s hard to know if the algorithm works by looking at just one earthquake. In this paper, after algorithm analysis of OLR signals based on martingale theory during the 10 years from 2009 to 2018, a time series analysis of re-anomaly screening for the 10 years’ data is proposed. The experimental results show that this method can be more effective in the statistical analysis of historical data and further improve the reliability of prediction.

Analysis and Design of Helical Antenna for Capture of Meteorological Images of NOAA Satellites

This article presents the design, the characterization and the construction of a helical antenna in axial mode to receive weather images of Colombia transmitted from NOAA (National Oceanic and Atmospheric Administration) satellites. The antenna has been located in Bogota (latitude: 4°36'34''N, longitude: 74°04'54''W; 25822 m above sea level), it has received Automatic Picture Transmission (APT) signals of good quality with circular polarization on the right (RHCP); those ones have been fed to a sharp SDR (generic software RTL-SDR) and decoded through the WXtoImg software in the receiver. In order to improve the performance of the antenna, the helix has been built with a coaxial cable of 0.0047λ diameter (RG-213 / U), the length of the feed cable has been minimized to 55 cm, (less than λ/2), and an impedance coupling network using a series stub has been designed. The physical design has represented an important aspect, since some wrong dimensions have caused distortions in the received signal. The experimental results show radiation diagrams with a gain of eight dBi and bandwidth of 30 kHz with respect to 3dB at the central frequency of 137 MHz, which are comparable with the simulations and theoretical analysis. With the above, the design of the circuit is validated; it guarantees its performance and confirms the practical utility of the antenna.

Precipitation of Radiation Belt Electrons by EMIC Waves With Conjugated Observations of NOAA and Van Allen Satellites

AbstractIn this letter, we present unique conjugated satellite observations of MeV relativistic electron precipitation caused by electromagnetic ion cyclotron (EMIC) waves. On the outer boundary of the plasmasphere, the Van Allen probe observed EMIC waves. At ionospheric altitudes, the National Oceanic and Atmospheric Administration (NOAA) 16 satellite at the footprint of Van Allen probe simultaneously detected obvious flux enhancements for precipitating >MeV radiation belt electrons but not for precipitating <MeV electrons. Theoretical calculations of pitch angle diffusion coefficients for radiation belt electrons indicate that observed EMIC waves can solely lead to flux enhancements of precipitating >MeV radiation belt electrons. Our result provides a direct magnetic conjugated observational link between in situ inner magnetospheric EMIC waves and precipitation of MeV relativistic electrons at ionospheric altitudes so as to reveal that EMIC waves can solely scatter MeV radiation belt electrons into the loss cone so as to precipitate into the atmosphere.

Effect of NOAA satellite orbital drift on AVHRR-derived phenological metrics

The U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center routinely produces and distributes a remote sensing phenology (RSP) dataset derived from the Advanced Very High Resolution Radiometer (AVHRR) 1-km data compiled from a series of National Oceanic and Atmospheric Administration (NOAA) satellites (NOAA-11, −14, −16, −17, −18, and −19). Each NOAA satellite experienced orbital drift during its duty period, which influenced the AVHRR reflectance measurements. To understand the effect of the orbital drift on the AVHRR-derived RSP dataset, we analyzed the impact of solar zenith angle (SZA) on the RSP metrics in the conterminous United States (CONUS). The AVHRR weekly composites were used to calculate the growing-season median SZA at the pixel level for each year from 1989 to 2014. The results showed that the SZA increased towards the end of each NOAA satellite mission with the highest increasing rate occurring during NOAA-11 (1989–1994) and NOAA-14 (1995–2000) missions. The growing-season median SZA values (44°–60°) in 1992, 1993, 1994, 1999, and 2000 were substantially higher than those in other years (28°–40°). The high SZA in those years caused negative trends in the SZA time series, that were statistically significant (at α=0.05 level) in 76.9% of the CONUS area. A pixel-based temporal correlation analysis showed that the phenological metrics and SZA were significantly correlated (at α=0.05 level) in 4.1–20.4% of the CONUS area. After excluding the 5 years with high SZA (>40°) from the analysis, the temporal SZA trend was largely reduced, significantly affecting less than 2% of the study area. Additionally, significant correlation between the phenological metrics and SZA was observed in less than 7% of the study area. Our study concluded that the NOAA satellite orbital drift increased SZA, and in turn, influenced the phenological metrics. Elimination of the years with high median SZA reduced the influence of orbital drift on the RSP time series.

Albedo and reflectance anisotropy retrieval from AVHRR operated onboard NOAA and MetOp satellites: Algorithm performance and accuracy assessment for Europe

In this study, the land surface albedo together with its Bidirectional Reflectance Distribution Function (BRDF) are retrieved for the years 2000 to 2012 from Local Area Coverage (LAC) surface reflectance data gathered by the Advanced Very High Resolution Radiometer (AVHRR) over Europe. For the retrieval the MODerate resolution Imaging Spectroradiometer (MODIS) BRDF/albedo processing scheme is employed. The comparatively high revisit frequency and high variability in angular sampling of the AVHRR sensors operated simultaneously onboard the different National Oceanic and Atmospheric Administration (NOAA) and Meteorological Operational (MetOp) satellites contribute substantially to the success and quality of BRDF retrieval. The performance of the BRDF model for AVHRR data is assessed by comparison to MODIS BRDF retrievals, and the AVHRR BRDF reflectance data is validated against BRDF reflectance data from MODIS and in situ data gathered at three field sites in Switzerland. The comparison shows that the AVHRR BRDF retrievals are of high quality across most of Europe. The higher angular sampling of AVHRR allows for more full model retrievals of best quality and generates less fill values compared to MODIS. For most of the years investigated, the absolute accuracy of the AVHRR albedos is found to be within 0.05 throughout the complete seasonal cycle with a minimum bias at the peak of the growing season. However, they systematically underestimate the field-measured albedos, predominantly in winter due to spatial scale mismatch in combination with site heterogeneity and because the expression for the calculation of satellite-based blue-sky albedo does not account for multiple scattering. A slightly increased underestimation also occurs during vegetation senescence, presumably because of the narrow to broadband conversion employing only two bands. Overall, the results confirm the potential of AVHRR to produce multi-decadal data sets of reflectance anisotropy and albedo for use in climate monitoring and modeling studies. This offers a promising and unique opportunity to produce a BRDF/albedo climate data record from AVHRR dating back to 1985.

Optical Passive Sensor Calibration for Satellite Remote Sensing and the Legacy of NOAA and NIST Cooperation.

This paper traces the cooperative efforts of scientists at the National Oceanic and Atmospheric Administration (NOAA) and the National Institute of Standards and Technology (NIST) to improve the calibration of operational satellite sensors for remote sensing of the Earth’s land, atmosphere and oceans. It gives a chronological perspective of the NOAA satellite program and the interactions between the two agencies’ scientists to address pre-launch calibration and issues of sensor performance on orbit. The drive to improve accuracy of measurements has had a new impetus in recent years because of the need for improved weather prediction and climate monitoring. The highlights of this cooperation and strategies to achieve SI-traceability and improve accuracy for optical satellite sensor data are summarized1.

Sleep disturbances and their relationship with excessive exposure to light at night: the Korean genome and epidemiology study

Although excessive artificial light at night is known to cause circadian disruption or nocturnal melatonin suppression, there was no systemic study which examined the association of light at night (LAN) and sleep disturbance. The aim of this study was performed to investigate how exposure to LAN can affect sleep in a population level. We carried out a cross-sectional study using the data from the Korean Genome and Epidemiology Study, which began in 2001 as an ongoing population-based study of Korean adults aged 39–70 years in Ansan and Ansung, South Korea. Using NOAA (national oceanic and atmospheric administration) satellite images, levels of LAN (0–63 W/cm 2 steradian) were dichotomized (high vs. low intensity) in both regions. The relationships between LAN exposure and sleep disturbances such as insomnia, excessive daytime sleepiness (EDS), insufficiency of sleep, snoring, irregularity of sleep, restless legs syndrome (RLS), and nap were investigated using multiple logistic regression. We estimated odds ratio (OR) and 95% confidence interval (CI) of LAN exposure parameter in each sleep disturbance using multiple logistic regression models adjusting for various clinical variables. The sample consisted of 8531 participants (4015 men and 4516 women) with mean age of 52.9 ± 9.0 years. The participants complained of insomnia (9.6%), EDS (13.7%), insufficiency of sleep (34.2%), snoring (62.7%), irregularity of sleep (26.3%), RLS (12.5%), and nap (37.8%). High LAN exposure was positively associated with insomnia (OR = 1.73, 95% CI [1.40, 2.14]) and snoring (OR = 1.20, 95% CI [1.06, 1.37]), but was negatively associated with EDS (OR = 0.73, 95% CI [0.61, 0.88]) and irregularity of sleep (OR = 0.65, 95% CI [0.57, 0.75]). On the other hand, LAN exposure was not associated with RLS, nap, or insufficiency of sleep. We concluded that high LAN exposure contributed to sleep disturbance such as insomnia and snoring. This study was supported by the KEITI of the Ministry of Environment of Korea (RQ201205099). This study was provided with biospecimens and data from the Korean Genome Analysis Project (4845–301), the Korean Genome and Epidemiology Study (4851–302), and Korea Biobank Project (4851–307, KBP- 2013–05) that were supported by the Korea Center for Disease Control, Republic of Korea. The light at night data was provided by the National Oceanic and Atmospheric Administration.

Spatial distribution characteristics of sea-ice-hazard risk in Bohai, China

AbstractSea-ice hazard causes serious harm to aquaculture, marine navigation, offshore oil production and other activities in the Bohai Sea, China. To study the spatial distribution characteristics of sea-ice-hazard risk in Bohai is therefore desirable. The thickness and area of sea ice in the Bohai Sea during the winters (December–March) of 1987–2011 were estimated using data from the NOAA (US National Oceanic and Atmospheric Administration) satellite. The sea-ice thickness was converted into a sea-ice-hazard index after defining this index, and the different sea-ice-hazard risk grades were classified. The occurrence probability of sea-ice hazard was also calculated using fuzzy risk theory, and the spatial distribution characteristics of sea-ice-hazard risk in the Bohai Sea were studied. The results show that the sea-ice-hazard risk for offshore aquaculture decreased as the offshore distance increased. All the oilfields in Liaodong Bay are influenced by sea-ice hazard, two of the fields in Bohai Bay are slightly affected and the remaining fields are not influenced. The risk for marine navigation is related to the location of the port and the distance from the port. The risk in the port area is the highest; it is reduced by more than 30% at distances 10 km away from the port.

Correcting AVHRR Long Term Data Record V3 estimated LST from orbital drift effects

NOAA (National Oceanic and Atmospheric Administration) satellite series is known to suffer from what is known as the orbital drift effect. The Long Term Data Record (LTDR [Pedelty et al., 2007]), which provides AVHRR (Advanced Very High Resolution Radiometer) data from these satellites for the 80s and the 90s, is also affected by this orbital drift. To correct this effect on Land Surface Temperature (LST) time series, a novel method is presented here, which consists in adjusting retrieved LST time series on the basis of statistical information extracted from the time series themselves. This method is as simple and straightforward as possible, in order to be implemented easily for such a large dataset as the LTDR. The correction is applied on a pixel by pixel basis, and relies on a 2nd order polynomial fit of per satellite solar zenithal angle (SZA) anomalies against time. If the pixel time series is identified as contaminated by the orbital drift for any of the different satellite active periods, LST anomalies are fitted linearly against both time and the 2nd order polynomial fit of SZA anomalies. This double fit allows for the removal of orbital drift influence without removing eventual trends in the signal, which is of utmost importance for vegetation change detection. When applied to simulated LST time series, this method shows errors comparable to the errors associated to LST estimation for most cases. When applied to LTDR LST time series, the approach normalizes the distribution of LST values at the beginning and end of each satellite activity period, and visual inspection of the time series does not show any residual orbital drift in the corrected LST time series. The approach also improves the significance of retrieved trends through the whole time span of the LTDR dataset. The application of this method to the whole LTDR dataset could lead to the compilation of the first coherent global dataset of land surface temperature.

NOAA budget would boost satellite funding but cut some key areas

The White House's proposed fiscal year (FY) 2013 budget for the National Oceanic and Atmospheric Administration (NOAA), announced on 13 February, looks favorable at first glance. The administration's request calls for $5.1 billion, an increase of $153 million (3.1%) above the FY 2012 estimated budget. However, the increase for NOAA satellites is $163 million, which means that other areas within the agency would be slated for decreased funding, including programs within the National Ocean Service (NOS), National Marine Fisheries Service (NMFS), National Weather Service (NWS), and some NOAA education programs. The proposed overall budget for the agency “reflects the overarching importance of weather satellites to public safety, to national security, and to the economy,” NOAA director Jane Lubchenco said at a 16 February briefing, noting that difficult choices were made regarding the budget. “Due to significant resources required for our weather satellites and the economic conditions in the country, other parts of our budget have been reduced, in some cases quite significantly,” she said. She added that the imperative to fund both the Joint Polar Satellite System (JPSS) and geostationary satellites in FY 2013 “imposes serious constraints on the rest of NOAA's budget.”

Correcting orbital drift signal in the time series of AVHRR derived convective cloud fraction using rotated empirical orthogonal function

Abstract. The Advanced Very High Resolution Radiometer (AVHRR) instruments onboard the series of National Oceanic and Atmospheric Administration (NOAA) satellites offer the longest available meteorological data records from space. These satellites have drifted in orbit resulting in shifts in the local time sampling during the life span of the sensors onboard. Depending upon the amplitude of the diurnal cycle of the geophysical parameters derived, orbital drift may cause spurious trends in their time series. We investigate tropical deep convective clouds, which show pronounced diurnal cycle amplitude, to estimate an upper bound of the impact of orbital drift on their time series. We carry out a rotated empirical orthogonal function analysis (REOF) and show that the REOFs are useful in delineating orbital drift signal and, more importantly, in subtracting this signal in the time series of convective cloud amount. These results will help facilitate the derivation of homogenized data series of cloud amount from NOAA satellite sensors and ultimately analyzing trends from them. However, we suggest detailed comparison of various methods and rigorous testing thereof applying final orbital drift corrections.

Soil moisture variations monitoring by AMSU-based soil wetness indices: A long-term inter-comparison with ground measurements

Soil moisture controls the partitioning of rainfall into runoff and infiltration and, consequently, the runoff generation. On the catchment scale its routine monitoring can be performed through remote sensing technologies. Within this framework, the purpose of this study is to investigate the potential of the Advanced Microwave Sounding Unit (AMSU), radiometer on board the NOAA (National Oceanic and Atmospheric Administration) satellites and operating since 1998, for the assessment of soil wetness conditions by comparing soil moisture data with both those measured in situ and provided by a continuous rainfall–runoff model applied to four catchments located in the Upper Tiber River (Central Italy). In particular, in order to perform a robust analysis an extensive and long-term period (nine years) of data was investigated. In detail, the Soil Wetness Variation Index, derived from the AMSU data modified in order to take account of the difference between the soil layer investigated by the satellite sensor and that used as a benchmark, was found to be correlated both with the in-situ and modeled soil moisture variations showing correlation coefficients in the range of 0.42–0.49 and 0.33–0.48, respectively. As far as the soil moisture temporal pattern is concerned, higher correlations were obtained (0.59–0.84 for the in-situ data and 0.82–0.87 for the modeled data set) partly due to the soil moisture seasonal pattern that enhances the correlation. Overall, the root mean square error was found to be less than 0.05m3/m3 for both the comparisons, thus assessing the potential of the AMSU sensor to quantitatively retrieve soil moisture temporal patterns. Moreover, the AMSU sensor can be considered as a useful tool to provide a reliable and frequently updated global soil moisture data set, considering its higher temporal resolution now available (about 4 passes per day) thanks to the presence of the sensor aboard different satellites.

Leaf-Shedding Phenology in Lowland Tropical Seasonal Forests of Cambodia as Estimated From NOAA Satellite Images

The seasonality of forest leaf area index and photosynthetic activity is an important element of ecosystem function. The ecohydrological models representing water and carbon fluxes in tropical forests need regional-scale spatial information on forest leaf phenology. We aimed to evaluate the utility of the National Oceanic and Atmospheric Administration (NOAA)/Advanced Very high resolution radiometer (AVHRR) imagery for monitoring evergreen and deciduous forest phenology in lowland Cambodia. The temporal sequences of the normalized difference vegetation index (NDVI) data were constructed from ten-day composite imagery acquired from May 2001 to April 2002. A local maximum fitting (LMF) technique that combined time-series filtering and local curve-fitting techniques was used to extract cloud-free data. With this approach, different phenology between evergreen and deciduous forests was clearly observed during the dry season: Deciduous forests exhibited marked and spatially uniform loss and (later) gain in new leaf area, whereas evergreen forests showed lesser and spatially/temporally heterogeneous changes in leaf phenology. A significant finding was that about 30% of evergreen forests shed detectable quantities of leaves at two times during a dry season (i.e., in the early, and late, dry season). In summary, the capability of composite NOAA/AVHRR NDVI data sets processed with the LMF technique was established as a viable approach for monitoring heterogeneous forest phenology at regional scales during the tropical dry season, although it is apparent that further improvements in cloud clearing are required if the approach is to be useful also in the wet season.