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I collect some open source aerospace softwares in this post. For present, my focus is on orbital and attitude dynamics, but also include some remote sensing data handeling and analysis.

Suggestions, recommendations, and comments are very welcomed.

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NASA Goddard Earth Sciences Data Information and Services Center (GESDISC)

AIRS on EOS Aqua (Atmospheric Infrared Sounder on NASA’s Aqua Satellite) (Level 3 product)

AIRS/Aqua L3 Daily Standard Physical Retrieval (AIRS-only) 1 degree x 1 degree V006 (AIRS3STD)

https://disc.gsfc.nasa.gov/datasets/AIRS3STD_006/summary?keywords=ozone
The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS Only Level 3 Daily Gridded Product contains standard retrieval means, standard deviations and input counts. Each file covers a temporal period of 24 hours for either the descending (equatorial crossing North to South @1:30 AM local time) or ascending (equatorial crossing South to North @1:30 PM local time) orbit. The data starts at the international dateline and progresses westward (as do the subsequent orbits of the satellite) so that neighboring gridded cells of data are no more than a swath of time apart (about 90 minutes). The two parts of a scan line crossing the dateline are included in separate L3 files, according to the date, so that data points in a grid box are always coincident in time. The edge of the AIRS Level 3 gridded cells is at the date line (the 180E/W longitude boundary). When plotted, this produces a map with 0 degrees longitude in the center of the image unless the bins are reordered. This method is preferred because the left (West) side of the image and the right (East) side of the image contain data farthest apart in time. The gridding scheme used by AIRS is the same as used by TOVS Pathfinder to create Level 3 products. The daily Level 3 products have gores between satellite paths where there is no coverage for that day. The geophysical parameters have been averaged and binned into 1 x 1 deg grid cells, from -180.0 to +180.0 deg longitude and from -90.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean and can be used to generate custom multi-day maps from the daily gridded products. ~~ The thermodynamic parameters are: Skin Temperature (land and sea surface), Air Temperature at the surface, Profiles of Air Temperature and Water Vapor, Tropopause Characteristics, Column Precipitable Water, Cloud Amount/Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Reflectance, Emissivity, Surface Pressure, Cloud Vertical Distribution.~~ The trace gases parameters are: Total Amounts and Vertical Profiles of Carbon Monoxide, Methane, and Ozone. The actual names of the variables in the data files should be inferred from the Processing File Description document.

Get Data

https://airs.jpl.nasa.gov/data/readers-tools/
https://disc.gsfc.nasa.gov/information/documents?title=AIRS Documentation
AIRS Version 7 Level 3 Product User Guide
Batched readme files in tar fromat for IDL and Matlab swath and grid data.
Batched files in tar format, with sample Input/Output

AIRS/Aqua L3 Daily Support Product (AIRS-only) 1 degree x 1 degree V006 (AIRS3SPD)

https://disc.gsfc.nasa.gov/datasets/AIRS3SPD_006/summary?keywords=ozone

The L3 support products are similar to the L3 standard products but contain fields which are not fully validated, or are inputs or intermediary values. Because no quality control information is available for some of these fields, values from failed retrievals may be included.

AIRS/OMI (Level 2 and 3)

https://aura.gsfc.nasa.gov/omi.html

The Ozone Monitoring Instrument (OMI) instrument can distinguish between aerosol types, such as smoke, dust, and sulfates, and measures cloud pressure and coverage, which provides data to derive tropospheric ozone.

OMI derives its heritage from NASA’s Total Ozone Mapping Spectrometer (TOMS) instrument and the European Space Agency (ESA) Global Ozone Monitering Experiment (GOME) instrument (on the ERS-2 satellite). It can measure many more atmospheric constituents than TOMS and provides much better ground resolution than GOME (13 km x 25 km for OMI vs. 40 km x 320 km for GOME).

OMI measures ozone profiles (in the UV) complimentary to those measured by TES and HIRDLS (in the IR) and MLS (in the microwave).

OMI measures the total column amount of atmospheric ozone NO2 as well as lower atmospheric dust, smoke, and other aerosols.

OMI is a nadir-viewing wide-field-imaging spectrometer, giving daily global coverage.

Aura validation data center (AVDC)

https://avdc.gsfc.nasa.gov/index.php

Data archived at the AVDC originates from several special Aura validation campaigns, NASA aircraft and balloon deployments, …

OMPS-NPP L3 NM Ozone (O3) Total Column 1.0 deg grid daily V2 (OMPS_NPP_NMTO3_L3_DAILY)

https://disc.gsfc.nasa.gov/datasets/OMPS_NPP_NMTO3_L3_DAILY_2/summary?keywords=ozone

The OMPS-NPP L3 NM Ozone (O3) Total Column 1.0 deg grid daily product provides total ozone measurements from the Ozone Mapping and Profiling Suite (OMPS) Nadir-Mapper (NM) instrument on the Suomi-NPP satellite. The level-3 gridding algorithm is used to combine the orbital OMPS cross track measurements into a daily map product with a fixed global grid. Grid cells are computed as weighted averages of a given parameter derived for the field-of-views that overlay the given cell. The current version of this product includes UV aerosol index and reflectivity at 331 nm retrievals as well.

Each granule contains data for a full day. Spatial coverage is global (-90 to 90 degrees latitude), with a resolution of 1.0 degree in longitude and 1.0 degree in latitude, and array size of 360 by 180. The files are written using the Hierarchical Data Format Version 5 or HDF5.

README Document

JPL: AIRS/OMI Combined Products (Using Level 2 AIRS and OMI)

https://tes.jpl.nasa.gov/multi-instrument-products/airs-omi/

The AIRS/OMI level 2 (L2) ozone profile product has a spatial sampling and the retrieval characteristics of ozone profiles equivalent to TES L2 standard data product, demonstrating the feasibility of extending TES L2 data record via a multiple spectral retrieval approach.

We refer the reader to the TES Data Users’ Guide [Herman and Kulawik, Eds., 2018] for details on the data structure,
but recommend the user treat the joint AIRS/OMI version 1 as preliminary data


Mission

Mission Instrument Data product Operator/Provider/Maintainer
Arus AIRS L2, L3 Goddard
Arus OMI L2, L3 Goddard
Arus AIRS/OMI combined JPL
Suomi-NPP OMPS L3 Goddard (这个似乎是我想要的!!!)
Abbr. Full
OMI Ozone Monitoring Instrument
OMPS Ozone Mapping and Profiling Suite
OMPS/NM Nadir-Mapper

My conclusions

Zotero can do everything Mendeley could, even more elegantly. Simple DO NOT use Mendeley.

Sadly the development of Docear stopped, so don’t use it

Use tags to organize papers, see below for reasons. Leave collection just literally a collection of something.

Set up your own rules ahead and then follow them strictly. Keep polishing your rules as needed.

Working flow

Collecting efficiently

Add a new collection, search and add using the Zotero connector plugin for Chrome.

Download paper using sci-hub-fy plugin, if necessary.

Make sure meta-data are correct at the very beginning.

Files should be managed by ZotFile, saved in Dropbox.

Reading on tablet

ZotFile + Dropbox + Book Note+ or other tablets. Helps to concentrate on papers.

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  1. Beate Klinger, and Torsten Mayer-Gürr, “The role of accelerometer data calibration within GRACE gravity field recovery: Results from ITSG-Grace2016”, Advances in Space Research, vol. 58, Nov. 2016, pp. 1597–1609.

It should be noted that our calibration approach aims at removing effects of instrument imperfections (cf. Sections 4.2 and 4.3) on the gravity field recovery. Hence, the used calibration equation does not guarantee to model these imperfections (e.g. temperature-induced bias drifts, misalignment) in a physical correct way, and will probably also absorb other not-modeled effects.

In this paper we only show results for GRACE-A, as very similar results have been obtained for GRACE-B. 【间接说明两颗星是独立做的calibration】

(p.1058) This is the only link between what the instrument actually measures (e.g., radiance, in the form of digital counts) and what one wants to measure (e.g., radiance). (p.1058) Conversion to at-sensor spectral radiance and/or top-of-atmosphere (TOA) reflectance is the fundamental steps to compare products from different sensors.

Calibration 的分类

See TanDEM-X at eoportal

  • Internal calibration
  • Geometric calibration
  • Antenna pointing
  • Antenna model verification
  • Radiometric calibration
    • see here: Radiometric Calibration and Corrections
      • “Radiometric correction is done to calibrate the pixel values and/ correct for errors in the values. The process improves the interpretability and quality of remote sensed data. Radiometric calibration and corrections are particularly important when comparing multiple data sets over a period of time.”
      • “The value recorded for a given pixel includes not only the reflected or emitted radiation from the surface, but also the radiation scattered and emitted by the atmosphere. In most cases we are interested in only the actual surface values. To achieve these values, radiometric calibration and correction processes must be applied.”

Problem descriptions and definitions

Conventional or practical methods

Covariance Based Track Association (CBTA)

Geometrical approach

Xiangxu Lei, Kunpeng Wang, Pin Zhang, Teng Pan, Huaifeng Li, Jizhang Sang, and Donglei He, “A geometrical approach to association of space-based very short-arc LEO tracks”, Advances in Space Research, vol. 62, Aug. 2018, pp. 542–553.

space-based angles-only very short-arc (SBVSA) LEO tracks

Michalis K. Titsias, Magnus Rattray, and Neil D. Lawrence, “Markov chain Monte Carlo algorithms for Gaussian processes,” Bayesian Time Series Models, David Barber, A. Taylan Cemgil, and Silvia Chiappa, eds., Cambridge: Cambridge University Press, 2011, pp. 295–316. [Link].

Estimate latent function

f(x)f(\bm{x})

Observations

yi=fi+ϵiy_i = f_i + \epsilon_i

Joint distribution is

p(y,f)=p(yf)p(f)p(\bm{y},\bm{f}) = p(\bm{y}|\bm{f}) p(\bm{f})

Applying Bayes’ rule and posterior over f\bm{f} is

p(fy)=p(yf)p(f)p(yf)p(f)dfp(\bm{f}|\bm{y}) = \frac{p(\bm{y}|\bm{f})p(\bm{f})}{\int p(\bm{y}|\bm{f})p(\bm{f})\,{\rm d}\bm{f}}

Predict the function value f\bm{f}_* at an unseen inputs X\bm{X}_*

p(fy)=p(ff)p(fy)df\textcolor{blue}{p(\bm{f}_*|\bm{y})} = \int p(\bm{f}_*|\bm{f}) p(\bm{f}|\bm{y})\,{\rm d}\bm{f}

where p(ff)p(\bm{f}_*|\bm{f}) is the conditional GP prior given by,

p(ff)=N(f,)p(\bm{f}_*|\bm{f}) = \mathcal{N}(\bm{f}_*|\circ,\circ)

Predict y\bm{y}_* corresponding to f\bm{f}_* is

p(yy)=p(yf)p(fy)df\textcolor{red}{p(\bm{y}_*|\bm{y})} = \int p(\bm{y}_*|\bm{f}_*) \textcolor{blue}{p(\bm{f}_*|\bm{y})} \,{\rm d}\bm{f}_*

In a mainstream machine learning application involving large datasets and where fast inference is required, deterministic methods are usually preferred simply because they are faster.
In contrast, in applications related to scientific questions that need to be carefully addressed by carrying out a statistical data analysis, MCMC is preferred.

Rasmussen, Carl Edward, and Christopher K. I. Williams. 2006. Gaussian Processes for Machine Learning. Adaptive Computation and Machine Learning. Cambridge, Mass: MIT Press. http://www.gaussianprocess.org/gpml/chapters/.

笔记

Sec 2讲了做regression的几乎所有基础理论。

Sec 3讲做classification,没有看。

Sec 4讲不同covairance的性质,未看,待看。

在Sec 5讲模型的训练理论。 这本书里把通常的机器学习中的训练的概念称为model selection,所以作为一个外行花了很长时间才弄明白这部分是讲如何训练的。

Bayesian inference

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Mark E Pittelkau, “Survey of Calibration Algorithms for Spacecraft Attitude Sensors and Gyros”, Advances in the Astronautical Sciences, vol. 129, 2007, pp. 1–55.

1. Introduction

The purpose of this paper is to present an overview of the various calibration algorithms, to examine their merits, and to show where and how they have been applied.

This survey extends back to 1969, although there were some relatively minor developments before that time.

This survey focuses mainly on methods rather than applications.

A critical review of the literature is provided, including strengths and weaknesses of algorithms and an assessment of results and conclusions in the literature.

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