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We created an agent-based model (ABM; a computational model) for human-whale interactions in the North Atlantic area. We specifically looked at boats, lobster lines, and rogue fishnet interactions with the North Atlantic right whales (Eubalaena glacialis), and we assessed the conditions under which such interactions are more likely to result in injuries and fatalities for this critically endangered whale, such as the density of ships in the water. ABMs are a methodology particularly useful in data poor problems (where machine learning cannot be used), and are based on rules of interactions between various agents (human and nonhuman) and/or a specific physical environment; they can showcase and assess potential future scenarios. We informed our model with current data from the North Atlantic Right Whale Consortium and the National Oceanic and Atmospheric Administration and present our preliminary findings here. [ABSTRACT FROM AUTHOR]

The peak intensity that occurs during a storm event can drive the performance of a green stormwater infrastructure (GSI), which may or may not align with the expected performance of the GSI. Both the peak intensity volume and where it occurs within an event are found to influence the GSI response. The design criteria set the expectation of how well a GSI will manage stormwater within a watershed. The Villanova University bioinfiltration rain garden (BRG) has been monitored since 2003, providing a long hydrological data record that is used to study local and observed rainfall patterns in comparison to design criteria to understand the impact of storm intensity on GSI performance. Intensities for all the storms recorded at the site were assessed at different timesteps and compared to the intensities typically used by the design storm approach in meeting regulatory criteria. There were 1482 storm events analysed and for all timesteps, the values commonly used for meeting design regulations were seen to be well above what was observed at the BRG, with 98% of the storms occurring below these values. Out of the 1482 storms, only 46 storms (3%) had effective durations longer than 10 h and no storm observed had an effective duration longer than 22 h, yet their peak intensities were still below the peak intensity associated with design regulations. This finding highlights the difference in the duration these sites are designed to manage (typically 24 h), in comparison to the ones experienced by the systems. The peak intensity analysis done at the different timesteps shows that for the storms recorded at the BRG, the intensities vary with changing time intervals and events. Of all the assessed events, only two events recorded larger intensities than the regionally specified NOAA C design storm, demonstrating the skewness of the approach. There was no trend in peak rain intensities over the 20‐year rainfall record. This study concludes that due to their dynamic performance, vegetated GSI have a natural resilience to a variety of precipitation patterns and climate changes that may be compromised when designing to a static value set through design storms. [ABSTRACT FROM AUTHOR]

Rain rate influenced by atmospheric factors is related to rainfall patterns affected by climate change. Attenuation and signal losses due to rain are crucial constraints in communication systems such as 5G, microwave radio links, and communication satellite systems. These communication systems have been deteriorated due to signal fading and distortion when exposed to rain. Signal fading by hydrometeor particles in the atmosphere may occur during wireless signal transmissions at frequencies higher than 10 GHz. In this paper, Hydro-Estimator data for the Peninsular Malaysia, Sabah and Sarawak regions from 2011 to 2022 were extracted to determine the rain rate distribution, which is a crucial parameter for simulating and analyzing rain fade. Climate change affects rainfall distribution when signal transmission with higher frequency carries more data, thus resulting in a higher chance of signal loss and degradation. Long-term forecasts of the signal performance in 5G systems and high-frequency radio link frequencies can be developed by analyzing the effects of climate change based on the rainfall rate and the rain fade distribution. [ABSTRACT FROM AUTHOR]

The Load/Unload Response Ratio (LURR) is a seismic prediction method based on the dynamic evolution of the stress-strain relationship of rocks. By adopting Benioff strain as the response indicator, an in-depth analysis was conducted on the LURR anomalies within 400 km of the epicenter and its surrounding areas before the Ms7.4 earthquake in Madoi, Qinghai Province, on 22 May 2021. The analysis revealed that the LURR value peaked 1 month before the earthquake and then declined within half a month, indicating that the rock medium in the seismic gestation area was approaching the end of its yielding phase. Further investigation using the Outgoing Longwave Radiation (OLR) data from the National Oceanic and Atmospheric Administration (NOAA) satellite was conducted to study the short-term and imminent anomalous evolution of ground longwave radiation values after the high values declined (from May 14 to 25). The results showed that, spatially, significant and continuous OLR anomalies were observed only in the northern area of the epicenter before the earthquake, and these anomalies exhibited a trend of expanding towards the epicenter. Temporally, the entire anomalous evolution process can be divided into six phases: initial warming, anomaly expansion, peak intensity, anomaly weakening, earthquake occurrence, and anomaly disappearance. To validate this finding, the spatiotemporal evolution characteristics of LURR and OLR within 300 km of the epicenter and its surrounding areas before the Ms6.4 earthquake in Yangbi, Yunnan Province, on 21 May 2021, were analyzed, and similar patterns were found. These results suggest that the high LURR value before its decline may mark the end of the rock medium's yielding phase, and OLR data can reflect, to some extent, the state of tectonic stress accumulation along active faults in a critical condition. A comprehensive study of the anomalous evolution characteristics of these two physical parameters before the earthquake is not only conducive to the continuity from long-term to short-term forecasts but also of great significance for more accurately assessing the trend of seismic activity. [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) has an important local and remote influence on global climate through the distribution and transport of heat and moisture. As a result of climate forcing, significant changes occur in the SSTs, which result in many natural disasters such as supercharged storms, higher wind speeds, heavier precipitation and flooding. This study investigates the spatiotemporal changes and underlying atmospheric mechanisms of the Black Sea (BLS) surface temperature. For this purpose, National Oceanicand Atmospheric Administration (NOAA) high‐resolution SST data (0.25°), which were verified with buoy observations, were used for the period 1982–2021. To investigate the circulation impacts, the relationship between North Atlantic Oscillation and East Atlantic/West Russia (EA/WR) phases and SSTs of the western BLS (WBLS) and eastern BLS (EBLS) was analysed. According to the results, SST values increased from 1.64°C (in winter) to 2.52°C (in summer) during the 40‐year period. Significant SST increases are shown in the EBLS during the summer and fall months. Statistically significant negative correlations (p < 0.05) were found between EA/WR and winter (r = −0.57) and summer (r = −0.56) SSTs in the EBLS. During winter, surface high located in the eastern Anatolia causes southerly winds, which blows from the terrestrial areas to the EBLS and result in above‐normal SST values. During summer (under negative EA/WR phases), the Azores high‐pressure centre extends to the Balkan Peninsula and WBLS and as a consequence, a significant amount of moisture associated with high sea surface temperature (>27°C, above‐normal 2.0°C) develops low‐level moisture convergence. Proper synoptic conditions, strong instability conditions between the surface and upper levels, and orographic forcing enable the occurrence of convective cloud cells. The movement of these cells to the northeastern part of Turkiye by strong northwesterly winds causes extreme precipitation and associated flash‐flood events in a limited area where land–sea interaction occurs (i.e., Artvin, Rize and Hopa provinces of Turkiye). [ABSTRACT FROM AUTHOR]

Free-space optical (FSO) communication has become a focus of both study and commercial interest. FSO transfers data between sites in free space, typically the Earth's atmosphere or space, using laser-emitted light beams, in contrast to standard radio frequency (RF) wireless communication. The possibility for high data transmission speeds, the lack of licencing constraints, versatility, affordability, simplicity of deployment, and its environmentally friendly communication method which provides an alternative to crowded radio frequency-based communications are the driving forces behind this growing interest. To determine the feasibility of FSO communication in the cities of Alexandria and Aswan in Egypt, we utilized the micrometeorology model to analyze how weather conditions and atmospheric turbulence impact on FSO linkages based on real-time weather data. In both the winter and summer seasons, we evaluated the bit error rate, beam spreading, scintillation, and transmission range of FSO. Moreover, bit error rate performance was improved by the use of the multi-hop Decode and Forward relay. [ABSTRACT FROM AUTHOR]

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This study examines the severe dust storm from May 12-16/2022, culminating on May 15, with a comprehensive analysis and explanation. The weather maps from the National Oceanic and Atmospheric Administration (NOAA) and the European Centre for Medium-Range Weather Forecasts (ECMWF) weather maps were used to identify systems and patterns that contributed to the storiris activity continuity and tracking as well as maps of pressure compounds and wind vectors in levels 1000 and 850 mbar that appear with the dust state for the same days accompanied by tracking patterns in the middle of the turbosphere 500 mbar to give a comprehensive analytical view of climate conditions at each level of pressure and higher systems supportive of their persistence on the surface. The northwesterly winds are the main factor that carries dust over long distances. The eastern desert in Syria, the Empty Quarter in the Kingdom of Saudi Arabia, the desert of western Iran, and the desert region of western Iraq are among the main sources of dust In its atmosphere. Based on weather maps of the surface and upper levels of storm days, the concentration of dust reached very high levels in Iraqs airspace and surrounding countries, including Egypt Jordan, Lebanon, Palestine, and Syria. The intensity of the dust gradually decreased as the area was affected by wet westerly winds with relatively low temperatures and a relative increase in wind speeds due to the impact of the study area on the atmospheric decline centered around the Turkish island of Cyprus. [ABSTRACT FROM AUTHOR]

The Load/Unload Response Ratio (LURR) is a seismic prediction method based on the dynamic evolution of the stress-strain relationship of rocks. By adopting Benioff strain as the response indicator, an in-depth analysis was conducted on the LURR anomalies within 400 km of the epicenter and its surrounding areas before the Ms7.4 earthquake in Madoi, Qinghai Province, on 22 May 2021. The analysis revealed that the LURR value peaked 1 month before the earthquake and then declined within half a month, indicating that the rock medium in the seismic gestation area was approaching the end of its yielding phase. Further investigation using the Outgoing Longwave Radiation (OLR) data from the National Oceanic and Atmospheric Administration (NOAA) satellite was conducted to study the short-term and imminent anomalous evolution of ground longwave radiation values after the high values declined (from May 14 to 25). The results showed that, spatially, significant and continuous OLR anomalies were observed only in the northern area of the epicenter before the earthquake, and these anomalies exhibited a trend of expanding towards the epicenter. Temporally, the entire anomalous evolution process can be divided into six phases: initial warming, anomaly expansion, peak intensity, anomaly weakening, earthquake occurrence, and anomaly disappearance. To validate this finding, the spatiotemporal evolution characteristics of LURR and OLR within 300 km of the epicenter and its surrounding areas before the Ms6.4 earthquake in Yangbi, Yunnan Province, on 21 May 2021, were analyzed, and similar patterns were found. These results suggest that the high LURR value before its decline may mark the end of the rock medium’s yielding phase, and OLR data can reflect, to some extent, the state of tectonic stress accumulation along active faults in a critical condition. A comprehensive study of the anomalous evolution characteristics of these two physical parameters before the earthquake is not only conducive to the continuity from long-term to short-term forecasts but also of great significance for more accurately assessing the trend of seismic activity.

Abstract The atmospheric CO2 growth rate is a fundamental measure of climate forcing. NOAA's growth rate estimates, derived from in situ observations at the marine boundary layer (MBL), serve as the benchmark in policy and science. However, NOAA's MBL‐based method encounters challenges in accurately estimating the whole‐atmosphere CO2 growth rate at sub‐annual scales. Here we introduce the Growth Rate from Satellite Observations (GRESO) method as a complementary approach to estimate the whole‐atmosphere CO2 growth rate utilizing satellite data. Satellite CO2 observations offer extensive atmospheric coverage that extends the capability of the current NOAA benchmark. We assess the sampling errors of the GRESO and NOAA methods using 10 atmospheric transport model simulations. The simulations generate synthetic OCO‐2 satellite and NOAA MBL data for calculating CO2 growth rates, which are compared against the global sum of carbon fluxes used as model inputs. We find good performance for the NOAA method (R = 0.93, RMSE = 0.12 ppm year−1 or 0.25 PgC year−1). GRESO demonstrates lower sampling errors (R = 1.00; RMSE = 0.04 ppm year−1 or 0.09 PgC year−1). Additionally, GRESO shows better performance at monthly scales than the NOAA method (R = 0.76 vs. 0.47, respectively). Due to CO2's atmospheric longevity, the NOAA method accurately captures growth rates over 5‐year intervals. GRESO's robustness across partial coverage configurations (ocean or land data) shows that satellites can be promising tools for low‐latency CO2 growth rate information, provided the systematic biases are minimized using in situ observations. Along with accurate and calibrated NOAA in situ data, satellite‐derived growth rates can provide information about the global carbon cycle at sub‐annual scales.

The study of navigation solutions other than GNSS is becoming increasingly important due to growing concerns about the vulnerability of GNSS to interference. The use of existing LEO satellite signals is a promising option in this respect. This study analyzes the accuracy of Doppler shift measurements for three satellite systems: Iridium-next, Orbcomm, and NOAA, based on actual signal acquisition, and evaluates the potential of their combination in positioning. The study takes advantage of Orbcomm’s dual frequency capability to reduce Doppler shift measurement noise, while highlighting the irrelevance of the ionosphere-free combination with Orbcomm. It proposes a method for obtaining unambiguous Doppler shifts from NOAA’s FM-AM signal, and introduces a smoothing technique to further improve Doppler shift measurement accuracy of LEO satellites. The positioning results show that positioning with three constellations is more accurate than positioning with one or two constellations, and that the effectiveness of passively combining several LEO systems for positioning is limited by the disparity between satellite constellations and the measurement quality inherent in each system.

The first NOAA full-mission reanalysis (RAN1) of the sea surface temperature (SST) from the two Moderate Resolution Imaging Spectroradiometers (MODIS) onboard Terra (24 February 2000–present) and Aqua (4 July 2002–present) was performed. The dataset was produced using the NOAA Advanced Clear-Sky Processor for Ocean (ACSPO) enterprise SST system from Collection 6.1 brightness temperatures (BTs) in three MODIS thermal emissive bands centered at 3.7, 11, and 12 µm with a spatial resolution of 1 km at nadir. In the initial stages of reprocessing, several instabilities in the MODIS SST time series were observed. In particular, Terra SSTs and corresponding BTs showed three 'steps': two on 30 October 2000 and 2 July 2001 (due to changes in the MODIS operating mode) and one on 25 April 2020 (due to a change in its nominal blackbody temperature, BBT, from 290 to 285 K). Additionally, spikes up to several tenths of a kelvin were observed during the quarterly warm-up/cool-down (WUCD) exercises, when the Terra MODIS BBT was varied. Systematic gradual drifts of ~0.025 K/decade were also seen in both Aqua and Terra SSTs over their full missions due to drifting BTs. These calibration instabilities were mitigated by debiasing MODIS BTs using the time series of observed minus modeled ('O-M') BTs. The RAN1 dataset was evaluated via comparisons with various in situ SSTs. The data meet the NOAA specifications for accuracy (±0.2 K) and precision (0.6 K), often by a wide margin, in a clear-sky ocean domain of 19–21%. The long-term SST drift is typically less than 0.01 K/decade for all MODIS SSTs, except for the daytime 'subskin' SST, for which the drift is ~0.02 K/decade. The MODIS RAN1 dataset is archived at NOAA CoastWatch and updated monthly in a delayed mode with a latency of two months. Additional archival with NASA JPL PO.DAAC is being discussed. [ABSTRACT FROM AUTHOR]

The majority of natural products utilized to treat a diverse array of human conditions and diseases are derived from terrestrial sources. In recent years, marine ecosystems have proven to be a valuable resource of diverse natural products that are generated to defend and support their growth. Such marine sources offer a large opportunity for the identification of novel compounds that may guide the future development of new drugs and therapies. Using the National Oceanic and Atmospheric Administration (NOAA) portal, we explore deep-sea coral and sponge species inhabiting a segment of the U.S. Exclusive Economic Zone, specifically off the western coast of Florida. This area spans ~100,000 km2, containing coral and sponge species at sea depths up to 3000 m. Utilizing PubMed, we uncovered current knowledge on and gaps across a subset of these sessile organisms with regards to their natural products and mechanisms of altering cytoskeleton, protein trafficking, and signaling pathways. Since the exploitation of such marine organisms could disrupt the marine ecosystem leading to supply issues that would limit the quantities of bioactive compounds, we surveyed methods and technological advances that are necessary for sustaining the drug discovery pipeline including in vitro aquaculture systems and preserving our natural ecological community in the future. Collectively, our efforts establish the foundation for supporting future research on the identification of marine-based natural products and their mechanism of action to develop novel drugs and therapies for improving treatment regimens of human conditions and diseases. [ABSTRACT FROM AUTHOR]

It is an indisputable fact that coral reefs across the globe are in decline. With typical growth rates of 0.3 to 2 centimeters per year for massive corals, and up to 10 centimeters per year for branching corals, it can take up to 10,000 years for a coral reef to form (NOAA). The largest coral reef system in the United States, the Florida Reef Tract, is also the third largest barrier reef system in the world – only dwarfed by the Great Barrier Reef of Eastern Australia and the Mesoamerican Reef off the Caribbean coast of Central America. These ecosystems in the tropical Atlantic and Caribbean were once dominated by large branching coral species such as Acropora cervicornis and Acropora palmata, known better by their common names - Staghorn and Elkhorn respectively, both of which now find themselves listed as threatened under to the Endangered Species Act (Federal Register). However, due to a number of threats, populations of these coral species have declined precipitously and resulted in the National Atmospheric and Oceanic Administration and its partners to launch Mission: Iconic Reefs - one of the largest collaborative efforts on the planet to restore reefs along the Florida Reef Tract. As a result, there are a lot of questions surrounding the efficacy of coral reef restoration and the efforts taking place - mainly how the goal to restore such a large, devastated ecosystem will be achieved amidst hot topics like climate change and habitat destruction. This overarching goal of this project is to consolidate the latest available information surrounding coral reef restoration by providing a framework for practices such as micro-fragmentation, sexual reproduction and other research and restoration efforts occurring throughout the Florida Keys and beyond.This report is accompanied by a website that I created – Restoration Destination – a platform designed to provide viewers with an introduction to coral restoration efforts in the Florida Keys, how the Mission: Iconic Reefs effort plans to tackle the challenges and present ways for the community to get involved.

Los flujos verticales, dentro del ciclo hidrológico, son una de las variables de mayor relevancia en zona de llanura, dado que las pendientes varían entre 0 y 5%, y los flujos horizontales no son significativos. En este sentido, la evapotranspiración juega un rol fundamental en el manejo hídrico, ya que alrededor del 85% del agua que sale del sistema lo hace mediante este proceso, requiriendo una cuantificación precisa. El objetivo principal de este trabajo es calcular la evapotranspiración potencial y real (ETp y ETr) con datos de satélite y reanálisis mediante el uso de la plataforma Google Earth Engine. Para su cálculo se tomó la ecuación de Priestley-Taylor (PT) combinado con información de humedad de suelo, con una resolución espacio-temporal de 250 x 250 m cada ocho días, en la región pampeana argentina (RPA). El producto se valoró en siete estaciones de la RPA, cuyos resultados mostraron, para la ETp, un R-RMSE (error cuadrático medio robusto) de 0.5 mm d-1; un error sistemático (mediana) de 0.3 mm d-1, y el error aleatorio (RSD- desviación estándar robusta) de 0.5 mm d-1; mientras que para la ETr, estos valores son de 0.6, -0.2 y 0.5 mm d-1, respectivamente. Los resultados globales muestran que el método utilizado es una herramienta válida para caracterizar la ET en la RPA y que se puede usar para analizar su variabilidad espacio-temporal en diferentes condiciones extremas y realizar estudios ambientales aplicados.

Bondowoso Regency is declared to be at high risk for the threat of drought based on the IRBI map of the National Disaster Management Agency in 2020. This study aims to assess drought disasters based on ENSO and NOAA data. The proposed method for rainfall modeling was Statistical Downscaling (SD) using the Backpropagation Neural Network (BPNN), for which the output models were used for drought assessment using the EDI. The reliability test of the rainfall model is to compare the rainfall model with the observed rainfall. The reliability test of the EDI is to compare the results of the EDI analysis from the input rain model data with the observed rainfall data. ANN modeling results showed that monthly rainfall predictions are better. This is indicated by the R2 monthly, and 10-day base values of 0.97 and 0.83, respectively, with RMSE values of 0.05 and 0.07, and the best modeling in EDI analysis was R2 0.88 and 0.63 with RMSE 0.35 and 0.65. Based on the results of this study, it is shown that drought disaster assessment based on ENSO and NOAA climate data can be used as an alternative to support the decision-making system for drought mitigation.

This paper presents a labeling methodology for marine life data using a weakly supervised learning framework. The methodology iteratively trains a deep learning model using non-expert labels obtained from crowdsourcing. This approach enables us to converge on a labeled image dataset through multiple training and production loops that leverage crowdsourcing interfaces. We present our algorithm and its results on two separate sets of image data collected using the Seabed autonomous underwater vehicle. The first dataset consists of 10,505 images that were point annotated by NOAA biologists. This dataset allows us to validate the accuracy of our labeling process. We also apply our algorithm and methodology to a second dataset consisting of 3,968 completely unlabeled images. These image categories are challenging to label, such as sponges. Qualitatively, our results indicate that training with a tiny subset and iterating on those results allows us to converge to a large, highly annotated dataset with a small number of iterations. To demonstrate the effectiveness of our methodology quantitatively, we tabulate the mean average precision (mAP) of the model as the number of iterations increases.

This article investigates the possible permanent vegetation cover (VC) change over an extended time for five municipal regions in South Africa by applying satellite-acquired remote sensed normalized difference vegetation index (NDVI) values within a geographic information system (GIS), spatial (West Coast District) and time (1981 to 2019 and 2000 to 2020) context. The NDVI index measures surface reflectance and give a quantitative estimation of vegetation growth and biomass. The study found relevance in its application since VC change detection has taken prominence over the past number of years in terms of sustainable development. Methods of analysis include image mapping, temporal image differencing, Moran I statistic, and the Mann-Kendall trend test. In the main areas that recorded significant changes in their NDVI values (plus or minus 0.4 difference on their original NDVI value) over time, in general, have experienced substantial and permanent VC change. These areas are also spatially clustered and concentrated within specific areas within the wider district. However, these areas constitute only a minority of areas (less than 20%), whereas most of the areas within the district did not experience such significant and permanent change in VC. Instead, the changes that did occur in these majority of areas were related to seasonal variation, i.e., temporal changes.

The majority of natural products utilized to treat a diverse array of human conditions and diseases are derived from terrestrial sources. In recent years, marine ecosystems have proven to be a valuable resource of diverse natural products that are generated to defend and support their growth. Such marine sources offer a large opportunity for the identification of novel compounds that may guide the future development of new drugs and therapies. Using the National Oceanic and Atmospheric Administration (NOAA) portal, we explore deep-sea coral and sponge species inhabiting a segment of the U.S. Exclusive Economic Zone, specifically off the western coast of Florida. This area spans ~100,000 km2, containing coral and sponge species at sea depths up to 3000 m. Utilizing PubMed, we uncovered current knowledge on and gaps across a subset of these sessile organisms with regards to their natural products and mechanisms of altering cytoskeleton, protein trafficking, and signaling pathways. Since the exploitation of such marine organisms could disrupt the marine ecosystem leading to supply issues that would limit the quantities of bioactive compounds, we surveyed methods and technological advances that are necessary for sustaining the drug discovery pipeline including in vitro aquaculture systems and preserving our natural ecological community in the future. Collectively, our efforts establish the foundation for supporting future research on the identification of marine-based natural products and their mechanism of action to develop novel drugs and therapies for improving treatment regimens of human conditions and diseases.

The first NOAA full-mission reanalysis (RAN1) of the sea surface temperature (SST) from the two Moderate Resolution Imaging Spectroradiometers (MODIS) onboard Terra (24 February 2000–present) and Aqua (4 July 2002–present) was performed. The dataset was produced using the NOAA Advanced Clear-Sky Processor for Ocean (ACSPO) enterprise SST system from Collection 6.1 brightness temperatures (BTs) in three MODIS thermal emissive bands centered at 3.7, 11, and 12 µm with a spatial resolution of 1 km at nadir. In the initial stages of reprocessing, several instabilities in the MODIS SST time series were observed. In particular, Terra SSTs and corresponding BTs showed three ‘steps’: two on 30 October 2000 and 2 July 2001 (due to changes in the MODIS operating mode) and one on 25 April 2020 (due to a change in its nominal blackbody temperature, BBT, from 290 to 285 K). Additionally, spikes up to several tenths of a kelvin were observed during the quarterly warm-up/cool-down (WUCD) exercises, when the Terra MODIS BBT was varied. Systematic gradual drifts of ~0.025 K/decade were also seen in both Aqua and Terra SSTs over their full missions due to drifting BTs. These calibration instabilities were mitigated by debiasing MODIS BTs using the time series of observed minus modeled (‘O-M’) BTs. The RAN1 dataset was evaluated via comparisons with various in situ SSTs. The data meet the NOAA specifications for accuracy (±0.2 K) and precision (0.6 K), often by a wide margin, in a clear-sky ocean domain of 19–21%. The long-term SST drift is typically less than 0.01 K/decade for all MODIS SSTs, except for the daytime ‘subskin’ SST, for which the drift is ~0.02 K/decade. The MODIS RAN1 dataset is archived at NOAA CoastWatch and updated monthly in a delayed mode with a latency of two months. Additional archival with NASA JPL PO.DAAC is being discussed.

The 40+ years-long sea surface temperature (SST) dataset from 4 km Global Area Coverage (GAC) data of the Advanced Very High-Resolution Radiometers (AVHRR/2s and/3s) flown onboard ten NOAA satellites (N07/09/11/12/14/15/16/17/18/19) has been created under the NOAA AVHRR GAC SST Reanalysis 2 (RAN2) Project. The data were reprocessed with the NOAA Advanced Clear Sky Processor for Ocean (ACSPO) enterprise SST system. Two SST products are reported in the full ~3000 km AVHRR swath: 'subskin' (highly sensitive to true skin SST, but debiased with respect to in situ SST) and 'depth' (a closer proxy for in situ data, but with reduced sensitivity). The reprocessing methodology aims at close consistency of satellite SSTs with in situ SSTs, in an optimal retrieval domain. Long-term orbital and calibration trends were compensated by daily recalculation of regression coefficients using matchups with drifters and tropical moored buoys (supplemented by ships for N07/09), collected within limited time windows centered at the processed day. The nighttime Sun impingements on the sensor black body were mitigated by correcting the L1b calibration coefficients. The Earth view pixels contaminated with a stray light were excluded. Massive cold SST outliers caused by volcanic aerosols following three major eruptions were filtered out by a modified, more conservative ACSPO clear-sky mask. The RAN2 SSTs are available in three formats: swath L2P (144 10-min granules per 24 h interval) and two 0.02° gridded (uncollated L3U, also 144 granules/24 h; and collated L3C, two global maps per 24 h, one for day and one for the night). This paper evaluates the RAN2 SST dataset, with a focus on the L3C product and compares it with two other available AVHRR GAC L3C SST datasets, NOAA Pathfinder v5.3 and ESA Climate Change Initiative v2.1. Among the three datasets, the RAN2 covers the global ocean more completely and shows reduced regional and temporal biases, improved stability and consistency between different satellites, and in situ SSTs. [ABSTRACT FROM AUTHOR]

As drought occurs in different climates, assessment of drought impacts on parameters such as vegetation cover is of utmost importance. Satellite remote sensing images with various spectral and spatial resolutions represent information about different land covers such as vegetation cover. Hence, the purpose of this study was to investigate the performance of satellite vegetation indices to monitor the agricultural drought on a local scale. In this regard, satellite images including Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR) data were used to evaluate vegetation cover and their gradual changes effects on agricultural drought. Fars province in Iran with relatively low precipitation values was selected as the study area. Modified Perpendicular Drought Index (MPDI), MPDI1, Vegetation Condition Index (VCI), Normalized Difference Vegetation Index Anomalies (NDVIA), and Standardized Vegetation Index (SVI), were evaluated to select the remote sensing based index with the best performance in drought monitoring. The performance of such indices were investigated during 13 years (2000–2013) for MODIS and 29 years (1985–2013) for AVHRR. To assess the efficiency of the satellite indices in drought investigation, Standardized Precipitation Index (SPI) data of five selected stations were used for 3, 6, and 9 month periods on August. The results showed that NDVI-based vegetation indices had the highest correlation with SPI in cold climate and long-term timescale (6 and 9 month). The highest correlation values between remote sensing based indices and SPI were acquired, respectively, in 9-month and 6-month time-scales, with the values of 43.5% and 40%. Moreover, VCI showed the highest capability for agricultural drought investigating in different climate regions of the study area. Overall, the results proved that NDVI-based indices can be used for drought monitoring and assessment in a long-term timescale on a local time-scale. [ABSTRACT FROM AUTHOR]