Magnetic Field Measurements by the Satellite Systems: A Review

Recently, in real-time the Disturbance storm time (Dst) indices observing by Geostationary Operational Environmental Satellite (GOES) was performable using so-called Goes-Magnetometer. Dst index is a geomagnetic index, which is the L1 data with the lead time, to detect geomagnetic storms with the lead time. Geomagnetic storms affected human activity and caused economic losses. Therefore, Dst index is a very important index. The past recorded contributions of corresponding Satellites were introduced. Now, in real-time Dst indices observing by Geostationary Operational Environmental Satellite (GOES-16) (Goes-Magnetometer) was performed. However, the Dst index was not the issue in this study.

Applying machine learning methods to detect convection using Geostationary Operational Environmental Satellite-16 (GOES-16) advanced baseline imager (ABI) data

An ability to accurately detect convective regions is essential for initializing models for short-term precipitation forecasts. Radar data are commonly used to detect convection, but radars that provide high-temporal-resolution data are mostly available over land, and the quality of the data tends to degrade over mountainous regions. On the other hand, geostationary satellite data are available nearly anywhere and in near-real time. Current operational geostationary satellites, the Geostationary Operational Environmental Satellite-16 (GOES-16) and Satellite-17, provide high-spatial- and high-temporal-resolution data but only of cloud top properties; 1 min data, however, allow us to observe convection from visible and infrared data even without vertical information of the convective system. Existing detection algorithms using visible and infrared data look for static features of convective clouds such as overshooting top or lumpy cloud top surface or cloud growth that occurs over periods of 30 min to an hour. This study represents a proof of concept that artificial intelligence (AI) is able, when given high-spatial- and high-temporal-resolution data from GOES-16, to learn physical properties of convective clouds and automate the detection process. A neural network model with convolutional layers is proposed to identify convection from the high-temporal resolution GOES-16 data. The model takes five temporal images from channel 2 (0.65 µm) and 14 (11.2 µm) as inputs and produces a map of convective regions. In order to provide products comparable to the radar products, it is trained against Multi-Radar Multi-Sensor (MRMS), which is a radar-based product that uses a rather sophisticated method to classify precipitation types. Two channels from GOES-16, each related to cloud optical depth (channel 2) and cloud top height (channel 14), are expected to best represent features of convective clouds: high reflectance, lumpy cloud top surface, and low cloud top temperature. The model has correctly learned those features of convective clouds and resulted in a reasonably low false alarm ratio (FAR) and high probability of detection (POD). However, FAR and POD can vary depending on the threshold, and a proper threshold needs to be chosen based on the purpose.

Mitigation of Geostationary Lightning Mapper Geolocation Errors

The geolocation of lightning flashes observed by spaceborne optical sensors depends upon a priori assumptions of the cloud-top height (or, more generally, the height of the radiant emitter) as observed by the satellite. Lightning observations from the Geostationary Lightning Mappers (GLMs) on Geostationary Operational Environmental Satellite 16 (GOES-16) and GOES-17 were originally geolocated by assuming that the global cloud-top height can be modeled as an ellipsoidal surface with an altitude of 16 km at the equator and sloping down to 6 km at the poles. This method produced parallax errors of 20–30 km or more near the limb, where GLM can detect side-cloud illumination or below-cloud lightning channels at lower altitudes than assumed by the ellipsoid. Based on analysis of GLM location accuracy using a suite of alternate lightning ellipsoids, a lower ellipsoid (14 km at the equator, 6 km at the poles) was implemented in October and December 2018 for GLM-16 and GLM-17, respectively. While the lower ellipsoid slightly improves overall GLM location accuracy, parallax-related errors remain, particularly near the limb. This study describes the identification of optimized assumed emitter heights, defined as those that produce the closest agreement with the ground-based reference networks. Derived using the first year of observations from GOES-East position, the optimal emitter height varies geographically and seasonally in a manner consistent with known meteorological regimes. Application of the optimal emitter height approximately doubles the fraction of area near the limb for which peak location errors are less than half a GLM pixel.

A Statistical Model for Estimating Midday NDVI from the Geostationary Operational Environmental Satellite (GOES) 16 and 17

The newest version of the Geostationary Operational Environmental Satellite series (GOES-16 and GOES-17) includes a near infrared band that allows for the calculation of normalized difference vegetation index (NDVI) at a 1 km at nadir spatial resolution every five minutes throughout the continental United States and every ten minutes for much of the western hemisphere. The usefulness of individual NDVI observations is limited due to the noise that remains even after cloud masks and data quality flags are applied, as much of this noise is negatively biased due to scattering within the atmosphere. Fortunately, high temporal resolution NDVI allows for the identification of consistent diurnal patterns. Here, we present a novel statistical model that utilizes this pattern, by fitting double exponential curves to the diurnal NDVI data, to provide a daily estimate of NDVI over forests that is less sensitive to noise by accounting for both random observation errors and atmospheric scattering biases. We fit this statistical model to 350 days of observations for fifteen deciduous broadleaf sites in the United States and compared the method to several simpler potential methods. Of the days 60% had more than ten observations and were able to be modeled via our methodology. Of the modeled days 72% produced daily NDVI estimates with <0.1 wide 95% confidence intervals. Of the modeled days 13% were able to provide a confident NDVI value even if there were less than five observations between 10:00–14:00. This methodology provides estimates for daily midday NDVI values with robust uncertainty estimates, even in the face of biased errors and missing midday observations.

Gridded Satellite (GridSat) GOES and CONUS data

The Geostationary Operational Environmental Satellite (GOES) series is operated by the US National Oceanographic and Atmospheric Administration (NOAA). While in operation since the mid-1970s, the current series (GOES 8–15) has been operational since 1994. This document describes the Gridded Satellite (GridSat) data, which provide GOES data in a modern format. Four steps describe the conversion of original GOES data to GridSat data: (1) temporal resampling to produce files with evenly spaced time steps, (2) spatial remapping to produce evenly spaced gridded data (0.04∘ latitude), (3) calibrating the original data and storing brightness temperatures for infrared (IR) channels and reflectance for the visible channel, and (4) calculating spatial variability to provide extra information that can help identify clouds. The GridSat data are provided on two separate domains: GridSat-GOES provides hourly data for the Western Hemisphere (spanning the entire GOES domain) and GridSat-CONUS covers the contiguous US (CONUS) every 15 min (dataset reference: https://doi.org/10.7289/V5HM56GM).