Investigation of GPS precise relative static positioning during periods of ice clouds and snowfall precipitation

1993 ◽  
Vol 31 (1) ◽  
pp. 295-299 ◽  
Author(s):  
J.M. Tranquilla ◽  
H.M. Al-Rizzo
Keyword(s):  
Science ◽  
1973 ◽  
Vol 182 (4110) ◽  
pp. 381-383 ◽  
Author(s):  
R. J. Curran ◽  
B. J. Conrath ◽  
R. A. Hanel ◽  
V. G. Kunde ◽  
J. C. Pearl

2015 ◽  
Vol 8 (5) ◽  
pp. 1935-1949 ◽  
Author(s):  
A. Kylling ◽  
N. Kristiansen ◽  
A. Stohl ◽  
R. Buras-Schnell ◽  
C. Emde ◽  
...  

Abstract. Volcanic ash is commonly observed by infrared detectors on board Earth-orbiting satellites. In the presence of ice and/or liquid-water clouds, the detected volcanic ash signature may be altered. In this paper the sensitivity of detection and retrieval of volcanic ash to the presence of ice and liquid-water clouds was quantified by simulating synthetic equivalents to satellite infrared images with a 3-D radiative transfer model. The sensitivity study was made for the two recent eruptions of Eyjafjallajökull (2010) and Grímsvötn (2011) using realistic water and ice clouds and volcanic ash clouds. The water and ice clouds were taken from European Centre for Medium-Range Weather Forecast (ECMWF) analysis data and the volcanic ash cloud fields from simulations by the Lagrangian particle dispersion model FLEXPART. The radiative transfer simulations were made both with and without ice and liquid-water clouds for the geometry and channels of the Spinning Enhanced Visible and Infrared Imager (SEVIRI). The synthetic SEVIRI images were used as input to standard reverse absorption ash detection and retrieval methods. Ice and liquid-water clouds were on average found to reduce the number of detected ash-affected pixels by 6–12%. However, the effect was highly variable and for individual scenes up to 40% of pixels with mass loading >0.2 g m−2 could not be detected due to the presence of water and ice clouds. For coincident pixels, i.e. pixels where ash was both present in the FLEXPART (hereafter referred to as "Flexpart") simulation and detected by the algorithm, the presence of clouds overall increased the retrieved mean mass loading for the Eyjafjallajökull (2010) eruption by about 13%, while for the Grímsvötn (2011) eruption ash-mass loadings the effect was a 4% decrease of the retrieved ash-mass loading. However, larger differences were seen between scenes (standard deviations of ±30 and ±20% for Eyjafjallajökull and Grímsvötn, respectively) and even larger ones within scenes. The impact of ice and liquid-water clouds on the detection and retrieval of volcanic ash, implies that to fully appreciate the location and amount of ash, hyperspectral and spectral band measurements by satellite instruments should be combined with ash dispersion modelling.


2010 ◽  
Vol 115 (D17) ◽  
Author(s):  
Zhibo Zhang ◽  
Steven Platnick ◽  
Ping Yang ◽  
Andrew K. Heidinger ◽  
Jennifer M. Comstock

2014 ◽  
Vol 143 ◽  
pp. 64-72 ◽  
Author(s):  
Q.-L. Min ◽  
R. Li ◽  
B. Lin ◽  
E. Joseph ◽  
V. Morris ◽  
...  

2014 ◽  
Vol 23 (2) ◽  
pp. 024204 ◽  
Author(s):  
Shou-Ting Gao ◽  
Xiao-Fan Li ◽  
Yu-Shu Zhou

2018 ◽  
Vol 18 (3) ◽  
pp. 1945-1975 ◽  
Author(s):  
Alyn Lambert ◽  
Michelle L. Santee

Abstract. We investigate the accuracy and precision of polar lower stratospheric temperatures (100–10 hPa during 2008–2013) reported in several contemporary reanalysis datasets comprising two versions of the Modern-Era Retrospective analysis for Research and Applications (MERRA and MERRA-2), the Japanese 55-year Reanalysis (JRA-55), the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-I), and the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (NCEP-CFSR). We also include the Goddard Earth Observing System model version 5.9.1 near-real-time analysis (GEOS-5.9.1). Comparisons of these datasets are made with respect to retrieved temperatures from the Aura Microwave Limb Sounder (MLS), Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) Global Positioning System (GPS) radio occultation (RO) temperatures, and independent absolute temperature references defined by the equilibrium thermodynamics of supercooled ternary solutions (STSs) and ice clouds. Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) observations of polar stratospheric clouds are used to determine the cloud particle types within the Aura MLS geometric field of view. The thermodynamic calculations for STS and the ice frost point use the colocated MLS gas-phase measurements of HNO3 and H2O. The estimated bias and precision for the STS temperature reference, over the 68 to 21 hPa pressure range, are 0.6–1.5 and 0.3–0.6 K, respectively; for the ice temperature reference, they are 0.4 and 0.3 K, respectively. These uncertainties are smaller than those estimated for the retrieved MLS temperatures and also comparable to GPS RO uncertainties (bias  <  0.2 K, precision  >  0.7 K) in the same pressure range. We examine a case study of the time-varying temperature structure associated with layered ice clouds formed by orographic gravity waves forced by flow over the Palmer Peninsula and compare how the wave amplitudes are reproduced by each reanalysis dataset. We find that the spatial and temporal distribution of temperatures below the ice frost point, and hence the potential to form ice polar stratospheric clouds (PSCs) in model studies driven by the reanalyses, varies significantly because of the underlying differences in the representation of mountain wave activity. High-accuracy COSMIC temperatures are used as a common reference to intercompare the reanalysis temperatures. Over the 68–21 hPa pressure range, the biases of the reanalyses with respect to COSMIC temperatures for both polar regions fall within the narrow range of −0.6 K to +0.5 K. GEOS-5.9.1, MERRA, MERRA-2, and JRA-55 have predominantly cold biases, whereas ERA-I has a predominantly warm bias. NCEP-CFSR has a warm bias in the Arctic but becomes substantially colder in the Antarctic. Reanalysis temperatures are also compared with the PSC reference temperatures. Over the 68–21 hPa pressure range, the reanalysis temperature biases are in the range −1.6 to −0.3 K with standard deviations  ∼  0.6 K for the CALIOP STS reference, and in the range −0.9 to +0.1 K with standard deviations  ∼  0.7 K for the CALIOP ice reference. Comparisons of MLS temperatures with the PSC reference temperatures reveal vertical oscillations in the MLS temperatures and a significant low bias in MLS temperatures of up to 3 K.


2008 ◽  
Vol 35 (7) ◽  
pp. n/a-n/a ◽  
Author(s):  
R. John Wilson ◽  
Stephen R. Lewis ◽  
Luca Montabone ◽  
Michael D. Smith

2021 ◽  
Author(s):  
Alex Innanen ◽  
Brittney Cooper ◽  
Charissa Campbell ◽  
Scott Guzewich ◽  
Jacob Kloos ◽  
...  

&lt;p&gt;1. INTRODUCTION&lt;/p&gt;&lt;p&gt;The Mars Science Laboratory (MSL) is located in Gale Crater (4.5&amp;#176;S, 137.4&amp;#176;E), and has been performing cloud observations for the entirety of its mission, since its landing in 2012 [eg. 1,2,3]. One such observation is the Phase Function Sky Survey (PFSS), developed by Cooper et al [3] and instituted in Mars Year (MY) 34 to determine the scattering phase function of Martian water-ice clouds. The clouds of interest form during the Aphelion Cloud Belt (ACB) season (L&lt;sub&gt;s&lt;/sub&gt;=50&amp;#176;-150&amp;#176;), a period of time during which there is an increase in the formation of water-ice clouds around the Martian equator [4]. The PFSS observation was also performed during the MY 35 ACB season and the current MY 36 ACB season.&lt;/p&gt;&lt;p&gt;Following the MY 34 ACB season, Mars experienced a global dust storm which lasted from L&lt;sub&gt;s&lt;/sub&gt;~188&amp;#176; to L&lt;sub&gt;s&lt;/sub&gt;~250&amp;#176; of that Mars year [5]. Global dust storms are planet-encircling storms which occur every few Mars years and can significantly impact the atmosphere leading to increased dust aerosol sizes [6], an increase in middle atmosphere water vapour [7], and the formation of unseasonal water-ice clouds [8]. While the decrease in visibility during the global dust storm itself made cloud observation difficult, comparing the scattering phase function prior to and following the global dust storm can help to understand the long-term impacts of global dust storms on water-ice clouds.&lt;/p&gt;&lt;p&gt;2. METHODS&lt;/p&gt;&lt;p&gt;The PFSS consists of 9 cloud movies of three frames each, taken using MSL&amp;#8217;s navigation cameras, at a variety of pointings in order to observe a large range of scattering angles. The goal of the PFSS is to characterise the scattering properties of water-ice clouds and to determine ice crystal geometry.&amp;#160; In each movie, clouds are identified using mean frame subtraction, and the phase function is computed using the formula derived by Cooper et al [3]. An average phase function can then be computed for the entirety of the ACB season.&lt;/p&gt;&lt;p&gt;&lt;img src=&quot;https://contentmanager.copernicus.org/fileStorageProxy.php?f=gnp.eda718c85da062913791261/sdaolpUECMynit/1202CSPE&amp;app=m&amp;a=0&amp;c=67584351a5c2fde95856e0760f04bbf3&amp;ct=x&amp;pn=gnp.elif&amp;d=1&quot; alt=&quot;Figure 1 &amp;#8211; Temporal Distribution of Phase Function Sky Survey Observations for Mars Years 34 and 35&quot; width=&quot;800&quot; height=&quot;681&quot;&gt;&lt;/p&gt;&lt;p&gt;Figure 1 shows the temporal distributions of PFSS observations taken during MYs 34 and 35. We aim to capture both morning and afternoon observations in order to study any diurnal variability in water-ice clouds.&lt;/p&gt;&lt;p&gt;3. RESULTS AND DISCUSSION&lt;/p&gt;&lt;p&gt;There were a total of 26 PFSS observations taken in MY 35 between L&lt;sub&gt;s&lt;/sub&gt;~50&amp;#176;-160&amp;#176;, evenly distributed between AM and PM observations. Typically, times further from local noon (i.e. earlier in the morning or later in the afternoon) show stronger cloud features, and run less risk of being obscured by the presence of the sun. In all movies in which clouds are detected, a phase function can be calculated, and an average phase function determined for the whole ACB season. &amp;#160;&lt;/p&gt;&lt;p&gt;Future work will look at the water-ice cloud scattering properties for the MY 36 ACB season, allowing us to get more information about the interannual variability of the ACB and to further constrain the ice crystal habit. The PFSS observations will not only assist in our understanding of the long-term atmospheric impacts of global dust storms but also add to a more complete image of time-varying water-ice cloud properties.&lt;/p&gt;


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