NOAA Satellite Research Articles

Radiometric consistency between GOES-16 ABI and VIIRS on Suomi NPP and NOAA-20

NOAA has both geostationary (GEO) and polar orbiting (LEO) satellites that are procured through different vendors. For long-term environmental studies, it is essential to establish consistent radiometric calibration among satellite instruments irrespective of the satellite platforms. The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (S-NPP) and NOAA-20 satellites has been providing critical weather and climate-related data. With the launch of NOAA-20 in November 2017, the global coverage of VIIRS has doubled. With more than 7 years in space, S-NPP VIIRS has been rigorously calibrated and validated. We aim to quantify the radiometric consistency between the two VIIRS instruments through double differencing using GOES-16 Advanced Baseline Imager (ABI) as a reference instrument. We also provide insight into the temporal radiometric consistency between VIIRS and ABI. Since no direct simultaneous nadir overpasses (SNOs) exist between SNPP and NOAA-20 for intercomparison, GEO-LEO intercalibration is performed using SNOs between ABI and VIIRS instruments. We show that the NOAA-20 VIIRS measured top-of-atmosphere (TOA) reflectance is consistently lower than that of the S-NPP, mostly on the order of 2% to 3%, consistent with the past studies. GOES-16 ABI-observed reflectance is higher than both VIIRS instruments. SNOs over the all-sky tropical ocean are analyzed to quantify the bias for both NOAA-20 and S-NPP VIIRS relative to ABI. The impact on bias due to spectral differences is accounted for using spectral band adjustment factors estimated using hyperspectral measurements from SCIAMACHY. Uncertainties exist mainly due to calibration uncertainties in VIIRS and ABI, the time difference between the VIIRS and ABI observations, differences in cloud contamination, spectral response function differences, and lack of in-situ hyperspectral data to account for the spectral bias.

Multisource Assessments of the FengYun-3D Microwave Humidity Sounder (MWHS) On-Orbit Performance

The microwave humidity sounder (MWHS) onboard the Fengyun-3D satellite is providing the data for profiling atmospheric temperature and moisture and has become an important data source for improving the weather forecasts. In this article, three data sources are utilized for assessing the MWHS on-orbit performance, including Global Navigation Satellite System Occultation Sounder (GNOS), ECMWF (European Centre for Medium-Range Weather Forecasts) Re-Analysis (ERA)-Interim reanalysis, and Advanced Technology Microwave Sounder (ATMS) data. GNOS-retrieved atmospheric profiles and the reanalysis data are used as inputs to the community radiative transfer model (CRTM) for simulating the MWHS brightness temperatures at the top of the atmosphere in July 2018 for characterizing the instrument performance. Since ATMS is a well-calibrated microwave sounding instrument onboard both Suomi NPP and NOAA-20 satellites, its measurements are also collocated with MWHS data for a consensus analysis using the simultaneous nadir overpasses (SNOs) method. In comparing GNOS simulations, MWHS upper air temperature sounding channels (3–6) have relatively larger biases (less than 2.5 K) than the water vapor sounding channels. However, the standard deviation of the difference between observations and simulations (O-B) is larger for water vapor sounding channels. For ERA simulations, MWHS sounding channels exhibit negative biases similar to GNOS results but the standard deviation of O-B at the water vapor channels is much smaller. When compared with ATMS water vapor channels, MWHS biases are mostly negative and agree with those from ERA simulation. Thus, the large uncertainty in simulating MWHS water vapor sounding channels from GNOS could result from the poor input water vapor profiles and high water vapor variability in the lower troposphere.

Determination of the NOAA-20 VIIRS screen transmittance functions with both the yaw maneuver and regular on-orbit calibration data.

The Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the NOAA-20 satellite regularly performs on-orbit radiometric calibration of its reflective solar bands (RSBs) through observations of an onboard sunlit solar diffuser (SD). The incident sunlight passes through an attenuation screen (the SD screen) and then scatters off the SD to provide a radiance source for the calibration. The on-orbit change of the SD's bidirectional reflectance distribution function (BRDF), referred to as the H-factor, is determined by an onboard solar diffuser stability monitor (SDSM) whose eight detectors alternately observe the Sun through another attenuation screen (the SDSM screen) and the sunlit SD. The products of the SD screen transmittance and the BRDF at the mission start for both the SDSM and RSBs and the SDSM screen effective transmittance were measured prelaunch. Large unrealistic undulations in the retrieved H-factor were seen when using the prelaunch screen functions. To improve the accuracy of the retrieved H-factor, shortly after the satellite launch, 15 yaw maneuvers were performed to further characterize the screens. Although significantly improved, the H-factor derived using the screen functions determined from the yaw maneuver data still has large unrealistic undulations, revealing that the solar azimuth angular step size of the yaw maneuvers is too large. In this paper, we add high-quality regular on-orbit SD calibration data to the yaw maneuver data to further improve the relative product of the SD screen effective transmittance and the BRDF at the mission start for the SDSM and the SDSM screen relative effective transmittance. The H-factor time series derived from the newly determined screen transmittance functions is much smoother than that derived from using only the yaw maneuver data and thus considerably improves the radiometric calibration accuracy.

Value Uncertainty Analysis in Architecture and Trade Studies

A key element of architecture studies, analyses-of-alternatives, and related trade studies is a quantitative value model (VM). The VM maps stakeholder preferences for baskets of capabilities to a single number. The mapping ideally has properties that allow for cost-benefit calculations and quantitative trades. An important and difficult element in building a VM is quantifying uncertainty. Uncertainty may stem from uncertainty in stakeholder prioritization, inexact scoring of alternatives against the VM, and the effects of issues outside the VM. This article presents several methods of quantifying uncertainty in the VM resulting from these sources and assessing the sensitivity of preferred alternative architectures to this uncertainty. We discuss methods focused on value uncertainty related to stakeholder consensus, performance scoring, and stakeholder preference uncertainty. Importantly, we include approaches for modeling the strong correlations that exist between alternatives. The methods are applied to a real case study of satellite constellation architecture, the NOAA Satellite Observing System Architecture study, which contains all of the above-mentioned complexities.

The Climate Monitoring SAF Outgoing Longwave Radiation from AVHRR

Data from the Advanced Very High Resolution Radiometer (AVHRR) have been used to create several long-duration data records of geophysical variables describing the atmosphere and land and water surfaces. In the Climate Monitoring Satellite Application Facility (CM SAF) project, AVHRR data are used to derive the Cloud, Albedo, and Radiation (CLARA) climate data records of radiation components (i.a., surface albedo) and cloud properties (i.a., cloud cover). This work describes the methodology implemented for the additional estimation of the Outgoing Longwave Radiation (OLR), an important Earth radiation budget component, that is consistent with the other CLARA variables. A first step is the estimation of the instantaneous OLR from the AVHRR observations. This is done by regressions on a large database of collocated observations between AVHRR Channel 4 (10.8 µm) and 5 (12 µm) and the OLR from the Clouds and Earth’s Radiant Energy System (CERES) instruments. We investigate the applicability of this method to the first generation of AVHRR instrument (AVHRR/1) for which no Channel 5 observation is available. A second step concerns the estimation of daily and monthly OLR from the instantaneous AVHRR overpasses. This step is especially important given the changes in the local time of the observations due to the orbital drift of the NOAA satellites. We investigate the use of OLR in the ERA5 reanalysis to estimate the diurnal variation. The developed approach proves to be valuable to model the diurnal change in OLR due to day/night time warming/cooling over clear land. Finally, the resulting monthly mean AVHRR OLR product is intercompared with the CERES monthly mean product. For a typical configuration with one morning and one afternoon AVHRR observation, the Root Mean Square (RMS) difference with CERES monthly mean OLR is about 2 Wm−2 at 1° × 1° resolution. We quantify the degradation of the OLR product when only one AVHRR instrument is available (as is the case for some periods in the 1980s) and also the improvement when more instruments are available (e.g., using METOP-A, NOAA-15, NOAA-18, and NOAA-19 in 2012). The degradation of the OLR product from AVHRR/1 instruments is also quantified, which is done by “masking” the Channel 5 observations.

A study of the influence of tropospheric subsidence on spring and summer surface ozone concentrations at the JRC Ispra station in northern Italy

Abstract. The influence of tropospheric ozone on the surface ozone concentrations is investigated at the monitoring station of JRC Ispra, based on 10 years of measurements (2006–2015) of surface ozone data. In situ hourly measurements of ozone and other air pollutants, meteorological parameters, and weekly averaged 7Be (as an indicator of upper-tropospheric–stratospheric influence) and 210Pb measurements (as an indicator of boundary layer influence) have been used for the analysis. In addition, IASI + GOME-2 and IASI ozone satellite data have also been used. It is observed that frequently 7Be and ozone weekly peaks coincide, which might be explained by the impact of deep atmospheric subsidence on surface ozone, particularly during late spring and early summer. Based on this observation, a detailed analysis of selected 7Be and ozone episodes occurring during that period of the year has been performed in order to further elucidate the mechanisms of tropospheric influence on the surface pollutant concentrations. For the analysis, composite NOAA/ESRL reanalysis synoptic meteorological charts in the troposphere have been used as well as IASI satellite ozone measurements and NOAA HYSPLIT back trajectories. The JRC station hourly measurements during subsidence episodes show very low values of local pollution parameters (e.g., NOx, 222Rn, nephelometer data, PM10), close to zero. Conversely, during these periods ozone levels usually reach values around 45–60 ppb during the afternoon hours but also show significantly higher values than the average during the night and morning hours, which is a sign of direct tropospheric influence on the surface ozone concentrations.

Comparison of Advanced Technology Microwave Sounder Biases Estimated Using Radio Occultation and Hurricane Florence (2018) Captured by NOAA-20 and S-NPP

The second Advanced Technology Microwave Sounder (ATMS) was onboard the National Oceanic and Atmospheric Administration (NOAA)-20 satellite when launched on 18 November 2017. Using nearly six months of the earliest NOAA-20 observations, the biases of the ATMS instrument were compared between NOAA-20 and the Suomi National Polar-Orbiting Partnership (S-NPP) satellite. The biases of ATMS channels 8 to 13 were estimated from the differences between antenna temperature observations and model simulations generated from Meteorological Operational (MetOp)-A and MetOp-B satellites’ Global Positioning System (GPS) radio occultation (RO) temperature and water vapor profiles. It was found that the ATMS onboard the NOAA-20 satellite has generally larger cold biases in the brightness temperature measurements at channels 8 to 13 and small standard deviations. The observations from ATMS on both S-NPP and NOAA-20 are shown to demonstrate an ability to capture a less than 1-h temporal evolution of Hurricane Florence (2018) due to the fact that the S-NPP orbits closely follow those of NOAA-20.

NOAA Satellite performance in estimating rainfall over the Island of Lombok

Rainfall data is the main input data for use in hydrological analysis, especially for planning purposes. Rainfall data is usually obtained from observations through rain stations. However, the availability of rainfall recording stations is sometimes very limited and located far from the project location. Water resource projects often face this obstacle. A technique is needed for estimating rainfall data using rain data from remote sensing through weather satellite data information providers. Since rainfall data from satellites is not the real amount of measured rainfall on the land, the rainfall data from the satellite must still be evaluated as to whether it can be directly used to represent the rainfall data of a region. The aim of this study is to evaluate the validity of NOAA CPC daily total precipitation data by comparing it to measured rainfall data and to analyse the lag time using the cross-correlation method. The results showed that the NOAA data has a lag time of +1 day over Lombok Island. The rain estimated by NOAA positively correlated with gauged rainfall on the next day. However, the performance of the NOAA data was very poor and for prediction purposes, the data still need to be calibrated.

Atmospheric factors controlling the stable isotopes (δ18O and δ2H) of the Indian summer monsoon precipitation in a drying region of Eastern India

The Eastern part of India experiences a rapid reduction in rainfall in the past 20 years (1997–2017), which has several implications to the livelihood of the people and the ecosystems of the region. Stable isotopic analysis of precipitation along with extensive backward trajectory modelling was conducted in this drying region to understand the factors controlling the stable isotopic composition of precipitation and source and origin of atmospheric moisture for precipitation during the Indian Summer Monsoon (ISM). The drivers responsible for the isotopic variations in precipitation were evaluated using their statistical relations with temperature, amount of rainfall and Relative Humidity (RH). Additionally, Outgoing Longwave Radiation (OLR) data from NOAA satellites were used to evaluate the effect of convective activities on isotopic variations in precipitation. The pathways of moisture transport for each rain event were derived using backward trajectory model (HYSPLIT4.0) based on NCEP data and the atmospheric circulation patterns were evaluated using NCAR reanalysis data. Major sources of moisture causing rainfall during the ISM period in the drying region were evaluated using cluster analysis of the backward wind trajectories. The isotopic content in precipitation showed a weak “temperature and amount effects”; however, a moderate correlation was observed with relative humidity of the region. It is inferred that the relatively high isotopic depletion observed in the monsoon rains was not related to the amount of rainfall, but rather, to large scale convective processes. It is found that the rainwater isotopic content during the ISM appears to be controlled by the dynamics of moisture rather than the hydrometeorological conditions of the individual precipitation events. A Local Meteoric Water Line (δ2H = 7.86 δ18O + 8.81) was developed for Eastern India using the isotopic data of rainwater samples. Backward air mass trajectory modelling, cluster analysis of wind trajectories, atmospheric circulation patterns and lower D-excess suggest that the origin of moisture in the drying region of Eastern India was predominantly from the Arabian Sea and the contribution from the Bay of Bengal was insignificant.

Proton belt variations traced back to Fengyun-1C satellite observations

We used historical data to trace trapped protons observed by the Fengyun-1C (FY-1C) satellite at low Earth orbits (~800 km) and chose data at 5–10 MeV, 10–40 MeV, 40–100 MeV, and ~100–300 MeV from 25 March to 18 April 2000 to analyze the proton variations. Only one isolated strong storm was associated with a solar proton event during this period, and there was no influence from previous proton variations. Complex dynamic phenomena of proton trapping and loss were affected by this disturbance differently depending on the energy and L location. The flux of 5–10 MeV protons increased and created new trapping with a maximum at L ~2.0, and the peak flux was significantly higher than that at the center of the South Atlantic Anomaly. However, at higher L, the flux showed obvious loss, with retreat of the outer boundary from L ~2.7 to L ~2.5. The increase in the 10–40 MeV proton flux was similar to that of the 5–10 MeV flux; however, the peak flux intensity was lower than that at the center of the South Atlantic Anomaly. The loss of the 10–40 MeV proton flux was closer to the Earth side, and the outer boundary was reduced from L ~2.3 to L ~2.25. For the higher energy protons of 40–100 MeV and 100–300 MeV, no new trapping was found. Loss of the 40–100 MeV protons was observed, and the outer boundary shifted from L ~2.0 to L ~1.9. Loss was not obvious for the 100–400 MeV protons, which were distributed within L < 1.8. New proton trapping was more likely to be created at lower energy in the region of solar proton injection by the strong magnetic storm, whereas loss occurred in a wide energy range and reduced the outer boundary on the Earth side. Similar dynamic changes were observed by the NOAA-15 satellite in the same period, but the FY-1C satellite observed more complex changes in lower energy protons. These results revealed that the dynamic behavior of protons with different L-shells was due to differences in the pitch angle. Possible mechanisms related to new trapping and loss are also discussed. These mechanisms are very important for understanding the behavior of the proton belt in the coming solar cycle.

ПРОСТРАНСТВЕННЫЕ РАЗЛИЧИЯ ПЛОТНОСТИ РАЗРЫВОВ В ЛЕДЯНОМ ПОКРОВЕ ПРИАТЛАНТИЧЕСКОЙ ЧАСТИ АРКТИЧЕСКОГО БАССЕЙНА

We analyzed data on the spatial distribution (density) of large breaks (gaps) in the drifting sea ice cover in the Western Arctic for the period from October 2005 to September 2017, obtained through decoding of lowresolution images from the NOAA satellites. The specific length of gaps, which is the total length of them over an area of 1 km2, is used as a characteristic of the spatial density. It was found that along the continental slope, approximately from the meridian 70° E to the Lincoln Sea, there is a well-defined area of high density, which remains throughout most part of the ice cycle. In this area, the values of the specific gap length averaged over two-month periods exceeded 24 m/km2. In the near-polar region, the density of breaks was smaller throughout the whole ice cycle. The least values of the specific length take place in May–June that is caused by changes in the general state of the ice cover. It was determined that the density of gaps in this area of the Arctic basin well correlated with the speed of wind drift of ice: the more intensive the drift, the larger the density. On the continental slope, two local zones with maximum values of the specific length of breaks reaching 32 m/km2 are considered. It is suggested that the stability of their location in space and time is connected with the increased influence of tidal processes on the deformation of the ice cover over local bottom elevations on the continental slope. A correlation between the bottom profile and the values of the specific length of the gaps along two conditional lines passing through the maximum value zones did show that the largest values of the density are noticed in areas with significant gradients of the depth.

A Convolutional Neural Network for Coastal Classification Based on ALOS and NOAA Satellite Data

Although coastal classification has been attended in recent years, it is still a complicated problem in quantitative geomorphological and hydrological sciences. Nowadays, the integration of deep learning in remote sensing and GIS analysis can quickly classify and detect different characteristics on both land and sea. Therefore, the authors proposed the use of a convolutional neural network (ConvNet) for coastal classification based on these technologies and geomorphic profile graphs. The primary input data is digital elevation/depth models obtained from ALOS and NOAA satellite. Eight hundred coastal samples representing eight types of coasts taken along the coastline in Vietnam were used for training and testing various ConvNets. As a result, three ConvNet models using three different optimizer functions were developed with the accuracies of about 98% and low values of the loss function. These models were used to classify 1029 in 1150 coasts (equal to 89%) in Vietnam. Nearly 11% of Vietnamese coasts could not be defined by three ConvNet models due to their complex geomorphic profile graphs, and require assessments of other natural components. The trained ConvNet models can potentially update new coastal types in different tropical countries towards coastal classification on national and global scales.

A Deep Learning Trained Clear-Sky Mask Algorithm for VIIRS Radiometric Bias Assessment

Clear-sky mask (CSM) is a crucial influence on the calculating accuracy of the sensor radiometric biases for spectral bands of visible, infrared, and microwave regions. In this study, a fully connected deep neural network (FCDN) was proposed to generate CSM for the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard Suomi National Polar-Orbiting Partnership (S-NPP) and NOAA-20 satellites. The model, well-trained by S-NPP data, was used to generate both S-NPP and NOAA-20 CSMs for the independent data, and the results were validated against the biases between the sensor observations and Community Radiative Transfer Model (CRTM) calculations (O-M). The preliminary result shows that the FCDN-CSM model works well for identifying clear-sky pixels. Both O-M mean biases and standard deviations were comparable with the Advance Clear-Sky Processor over Ocean (ACSPO) and were significantly better than a prototype cloud mask (PCM) and the case without a clear-sky check. In addition, by replacing CRTM brightness temperatures (BTs) with the atmosphere air temperature and water vapor contents as input features, the FCDN-CSM exhibits its potential to generate fast and accurate VIIRS CSM onboard follow-up Joint Polar Satellite System (JPSS) satellites for sensor calibration and validation before the physics-based CSM is available.

Design of Double Cross Dipole Antenna as NOAA Satellite Signal Receiver for Monitor Cloud Conditions Application

NOAA is a remote sensing satellite that contains informations about the earth, such as mapping land, sea and reporting weater conditions, cloud conditions and hotspots in an area. NOAA satellite works at the downlink frequency of 137-138 MHz. Double Cross Dipole antenna can be used as signal receiver antenna from NOAA satellite. Double cross dipole antenna is design to operate at frequency range of 137-138 MHz. simulation result of 137.62 MHz frequency generate return of loss as -20.726 dB, the value of VSWR 1.20 bandwidth of 82.32 Mhz, omnidirectional polarization and circular polarization. The result of measurement of double cross dipole antenna it was obtained return loss as-18.911 dB, value of VSWR of 1.255, bandwidth of 32.813 MHz. Double cross dipole antenna can be used to receive NOAA satellite signal in a form of voice. Voice data can be converted into image with the help of WXtoImgsoftware. The result of conversion was in a form of cloud condition in Indonesia area.

Atmospheric Front over the Caspian Sea Studied with SAR and Optical Images and Meteorological Data

An analysis of a synthetic aperture radar (SAR) image from the Radarsat-2 satellite acquired at the end of September 2013 has been carried out. A manifestation of the atmospheric front passing over the Northern Caspian Sea is found on the image. An analysis of SAR image using the data of the NOAA, Terra, and Aqua satellites; sub-satellite data; meteorological measurements; and reanalysis allows us to obtain not only qualitative but also quantitative information about the phenomenon displayed in the SAR data. The front had a characteristic set of radar signatures and was identified as a cold front; it had a narrow zone of frontal squalls and moved in the southeastern direction. Estimates of the front motion speed, wind speed in the regions before and after the front, and other characteristics have been obtained.

Evaluation of the Solar Radiation in a Seismic Zone

The solar radiation affects the life on Earth and it is important in meteorology, climatology, solar energy, agriculture, hydrology and seismology, too. The Sun warms the earth which reflects a part of the energy. An effect of tectonic stress increases the ground temperature that is radiated into the atmosphere. We study the possibility to use the variation of reflected energy in correlation with seismicity, radon concentration, CO2 emission, ULF radio waves, telluric currents, air ionization, temperature in borehole and acoustic noise. In this case, the solar radiation is a precursor factor. This research is important to study the effects of climate change. Analyzing the solar energy budget we find information about the atmosphere: aerosols, ozone, sulfur dioxide, carbon dioxide, nitrogen dioxide and pollution. Our multidisciplinary network (AeroSolSys) monitors Vrancea, a Romanian area characterized by deep earthquakes. For this study, we use two stations, Plostina and Vrancioaia, where we have a net radiometer and a pyranometer. The first sensor includes two pyranometers (up and low) and two pyrgeometers (up and low), a Pt-100 and a Thermistor. The spectral range covers both the Solar Radiation, 0.3 to 3 micrometers, and the Far Infrared Radiation, 4.5 to 42 micrometers. In this case, we measure and determine the temperature of sensor necessary for pyrgeometers, pyranometer short-wave radiation up and low EyU - EyL, pyrgeometers long-wave radiation up and low EgU - EgL, Albedo, net solar radiation NSR, net far infrared radiation NFIR, net total radiation NTR, sky temperature SkyT and, land surface temperature LST. In Vrancioaia, we have a pyranometer with range 0.3 – 2.8 micrometers and maxim 1600 W/m2. Two video cameras monitor the sky and the measurements are correlated with meteorological equipment installed in each location. We use information about solar activity from NOAA satellites and Copernicus CAMS. The humidity of ground is important in energy budget and it is estimated from meteorological data and telluric currents. The interval of analysis is 3 hours, between 2016.01.01 – 2018.05.31. It is difficult to notice the effect of tectonic stress since the daily and seasonal variations of solar radiation are high but global warming is obvious. Solar radiation monitoring helps us to understand other phenomena like radon emission or air ionization.

Retrieval Algorithm For Sea And land boundary temperature And Effect Of Atmospheric Radiation Correction

An algorithm have been presented to retrieve sea and land surface temperature from NOAA satellite radiative measurements. We used the atmospheric radiation in the adjacent sea area to correct the atmosphere in the land and sea area. Because the self-consistency atmospheric radiant information is adopted at the same time and condition, this method has important actual efficiency. Simultaneity, several atmosphere patterns are selected by using atmosphere MODTRAN radiative transfer model program, the different retrieval algorithms are contrasted. The results show that practical effect is good, correct and feasible.

A study of a two-dimensional scanned lunar image for Advanced Technology Microwave Sounder (ATMS) geometric calibration

Abstract. The NOAA-20 satellite was successfully launched on 18 November 2017. It carries five key instruments, including the Advanced Technology Microwave Sounder (ATMS). On 31 January 2018, the spacecraft performed a pitch-over maneuver operation, during which two-dimensional lunar scan observations were collected. In this study, a technique has been developed by which the ATMS on-orbit geometric calibration accuracy can be validated based on this lunar scan dataset. The fully calibrated data are fitted to the antenna pattern coordinates via a Gaussian function. The deviation in the center of the fit function from the origin of the frame is taken to be the boresight pointing error of the instrument. This deviation is further transformed to the Euler angle roll and pitch defined in the spacecraft coordinate system. The estimated ATMS boresight pointing Euler angle roll (pitch) is 0.05∘ (0.22∘) at K band, −0.07∘ (0.25∘) at Ka band, 0.02∘ (0.24∘) at V band, -0.07∘ (-0.08∘) at W band, and -0.04∘ (0.02∘) at G band. The results are validated by comparing them with those derived from the coastline inflection point method, showing a good correlation. For the sounding channels where the coastline method is inapplicable, the lunar scan method is still capable of delivering reasonable estimations of their geometric calibration errors.

In situ and remotely sensed temperature comparisons on a Central Pacific atoll

Climate-induced warming events increasingly threaten coral reefs, heightening the need for accurate quantification of baseline temperatures and thermal stress in these ecosystems. To assess the strengths and weaknesses of NOAA satellite sea surface temperature and in situ measurements, we compared 5 yr of these data on Kiritimati atoll, in the central equatorial Pacific. We find that (1) satellite measurements were similar to in situ measurements (~ 10 m depth), albeit slightly warmer, with measurements converging once above Kiritimati’s maximum monthly mean; (2) in situ loggers detected subsurface cooling events missed by satellites; (3) thermal baselines and anomalies were consistent around the island; and (4) in situ degree heating week (DHW) calculations were most comparable to NOAA DHWs when calculated using NOAA’s climatology. These results suggest that NOAA’s satellite products accurately reflect conditions on central Pacific reefs, but that in situ measurements can identify localized events, such as subsurface upwelling, that may be ecologically relevant for corals.

Global Detection of Long-Term (1982–2017) Burned Area with AVHRR-LTDR Data

This paper presents the first global burned area (BA) product derived from the land long term data record (LTDR), a long-term 0.05-degree resolution dataset generated from advanced very high resolution radiometer (AVHRR) images. Daily images were combined in monthly composites using the maximum temperature criterion to enhance the burned signal and eliminate clouds and artifacts. A synthetic BA index was created to improve the detection of the BA signal. This index included red and near infrared reflectance, surface temperature, two spectral indices, and their temporal differences. Monthly models were generated using the random forest classifier, using the twelve monthly composites of each year as the predictors. Training data were obtained from the NASA MCD64A1 collection 6 product (500 m spatial resolution) for eight years of the overlapping period (2001–2017). This included some years with low and high fire occurrence. Results were tested with the remaining eight years. Pixels classified as burned were converted to burned proportions using the MCD64A1 product. The final product (named FireCCILT10) estimated BA in 0.05-degree cells for the 1982 to 2017 period (excluding 1994, due to input data gaps). This product is the longest global BA currently available, extending almost 20 years back from the existing NASA and ESA BA products. BA estimations from the FireCCILT10 product were compared with those from the MCD64A1 product for continental regions, obtaining high correlation values (r2 &gt; 0.9), with better agreement in tropical regions rather than boreal regions. The annual average of BA of the time series was 3.12 Mkm2. Tropical Africa had the highest proportion of burnings, accounting for 74.37% of global BA. Spatial trends were found to be similar to existing global BA products, but temporal trends showed unstable annual variations, most likely linked to the changes in the AVHRR sensor and orbital decays of the NOAA satellites.