The NOAA Space Weather Follow On (SWFO) Program supports NOAA's goal of reducing the impact of severe space weather events, which responds to the 2020 Promoting Research and Observations of Space Weather to Improve the Forecasting of Tomorrow Act. The SWFO Program will ensure continuity of space weather operational Solar Wind and Coronal Mass Ejection (CME) data to its operational users. The SWFO Program includes a SWFO-L1 observatory which will host a Solar Wind Plasma Sensor, a Magnetometer, a SupraThermal Ion Sensor, and a Compact Coronagraph. The SWFO Program will take rideshare with NASA’s IMAP mission scheduled for FY 2025.
Cross-sensor compatibility of spectral vegetation indices (VIs) between Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (VIIRS) and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) was investigated using their near-coincident observation pairs obtained along overlapped orbital tracks across the globe for the year 2015. The “top-of-atmosphere (TOA)” and “top-of-canopy (TOC)” normalized difference vegetation indices (NDVIs), TOC-enhanced vegetation index (EVI), and TOC two-band EVI (EVI2) were investigated. For all four VIs, VIIRS and MODIS VIs were subject to systematic differences in which VIIRS VIs were higher than their MODIS counterparts. The overall systematic differences and uncertainties (measured as mean differences and root mean square differences, respectively) were small (0.010 to 0.020 VI units and 0.015 to 0.022 VI units, respectively). TOA NDVI cross-sensor differences were neither seasonally nor view zenith angle dependent, whereas TOC NDVI cross-sensor differences slightly varied seasonally, but were not view zenith angle dependent. TOC EVI and TOC EVI2 cross-sensor differences were view zenith angle dependent, where systematic differences increased with increasing view zenith angle and, for large view zenith angles, they were higher during the summer seasons. These results support the normalization of view zenith angles as a required step to extend the MODIS VI record with VIIRS data.
The Advanced Very High Resolution Radiometer (AVHRR) sensors onboard The National Oceanic and Atmospheric
Administration (NOAA) polar-orbiting satellites have been measuring electromagnetic radiation emitted by the Earth in
the visible (VIS), Near-Infrared (NIR) and Infrared (IR) portions of the electromagnetic spectrum for nearly 30 years.
The Global Vegetation Index Vegetation Health product (GVI-x VH) developed from the AVHRR dataset includes the
Brightness Temperature (BT) variable calculated from the IR channels, which in turn is used to estimate other
environmental variables such as Sea Surface Temperature (SST), Land Surface Temperature (LST), Temperature
Condition Index (TCI), and Vegetation Health Index (VTI) among others. However, the satellite measured IR radiances
need to be corrected with sufficient accuracy to minimize the uncertainty introduced by a host of sources such as the
atmosphere, stratospheric aerosols, and satellite orbital drift before being input into any algorithm to generate remotely
sensed products. In this research we have applied a statistical technique based on Empirical Distribution Functions
(EDF) to normalize the NOAA GVI-x BT records for the combined effect of the sources of uncertainty mentioned
above, avoiding the need for physics based corrections. The normalized results are tested to verify that the normalization
improves the data.
In this paper, we explore the performance of a bio-optical model used to estimate the water leaving radiance at 412nm
and under what conditions this constraint can improve retrieval of water leaving radiances in the VIS and NIR channels.
We first demonstrate that the bio-optical model performance is well modeled by Hydrolight simulations under coastal
water conditions and is particularly suitable for coastal waters and that the 412nm estimator is well correlated to insitu
measurements from SeaBASS. We then show that unlike prior uses, the bio-optical estimator can also be used not only
to improve retrieval for absorbing aerosols but can be used to improve retrieval errors when regional aerosol models are
not included. Furthermore, we explore the optimal a-posteri correction and show that the original n=6 coefficient used
for aloft absorbing aerosols may need to be refined.
Multiwavelength elastic lidar is often used to probe the aerosol profiles of the atmosphere. Normally, the atmosphere is considered homogeneous and an a-priori aerosol ratio is given for each wavelength channel which is then processed independently. However, it is clear that the multiwavelength retrieved backscatter profiles should contain information that can be used to estimate particle size distribution which may provide a new estimate to range dependant aerosol ratio profiles which can be repeated until convergence. In this paper, we illustrate the basic idea of using multiwavelength data using a two wavelength lidar to obtain local information on the lidar ratio which can be used to improve lidar profiling in inhomogeneous atmospheres and show that a key feature of any scheme is the monotonic dependance between the optical data ratio and the distribution parameter. In addition, we extend the approach to a prototypical Nd:YAG three wavelength (355, 532, 1064nm) lidar arrangement and show that while an iterative lidar procedure can be used to extract range dependant profiles, imprecision in the inversion procedure as well as error propagation of the lidar back integration can hamper convergence.
Multiwavelength elastic lidar is often used to probe the aerosol profiles of the atmosphere. Normally, the atmosphere is considered homogeneous and an a-priori aerosol ratio is given for each wavelength channel which is then processed independently. However, it is clear that the multiwavelength retrieved backscatter profiles should contain information that can be used to estimate particle size distribution which may provide a new estimate to range dependent aerosol ratio profiles which can be repeated until convergence. In this paper, we illustrate the basic idea of using multiwavelength data using a two wavelength lidar to obtain local information on the lidar ratio which can be used to improve lidar profiling in homogeneous atmospheres and show that a key feature of any scheme is the monotonic dependence between the optical data ratio and the distribution parameter. In addition, we extend the approach to a prototypical Nd:YAG three wavelength (355, 532, 1064 nm) lidar arrangement and show that while an iterative lidar procedure can be used to extract range dependent profiles, imprecision in the inversion procedure as well as error propagation of the lidar back integration can hamper convergence.
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