scholarly journals Assessing modelled spatial distributions of ice water path using satellite data

2010 ◽  
Vol 10 (5) ◽  
pp. 12185-12224 ◽  
Author(s):  
S. Eliasson ◽  
S. A. Buehler ◽  
M. Milz ◽  
P. Eriksson ◽  
V. O. John
Keyword(s):  
Spatial Distribution ◽  
Satellite Data ◽  
Climate Models ◽  
Data Sets ◽  
Data Set ◽  
Ice Water Path ◽  
Cloud Ice ◽  
The Tropics ◽  
Ipcc Ar4 ◽  
Ice Water

Abstract. The climate models used in the IPCC AR4 show large differences in monthly mean cloud ice. The most valuable source of information that can be used to potentially constrain the models is global satellite data. For this, the data sets must be long enough to capture the inter-annual variability of Ice Water Path (IWP). PATMOS-x was used together with ISCCP for the annual cycle evaluation in Fig. 7 while ECHAM-5 was used for the correlation with other models in Table 3. A clear distinction between ice categories in satellite retrievals, as desired from a model point of view, is currently impossible. However, long-term satellite data sets may still be used to indicate the climatology of IWP spatial distribution. We evaluated satellite data sets from CloudSat, PATMOS-x, ISCCP, MODIS and MSPPS in terms of monthly mean IWP, to determine which data sets can be used to evaluate the climate models. IWP data from CloudSat cloud profiling radar provides the most advanced data set on clouds. As CloudSat data are too short to evaluate the model data directly, it was mainly used here to evaluate IWP from the other satellite data sets. ISCCP and MSPPS were shown to have comparatively low IWP values. ISCCP shows particularly low values in the tropics, while MSPPS has particularly low values outside the tropics. MODIS and PATMOS-x were in closest agreement with CloudSat in terms of magnitude and spatial distribution, with MODIS being the best of the two. As PATMOS-x extends over more than 25 years and is in fairly close agreement with CloudSat, it was chosen as the reference data set for the model evaluation. In general there are large discrepancies between the individual climate models, and all of the models show problems in reproducing the observed spatial distribution of cloud-ice. Comparisons consistently showed that ECHAM-5 is the GCM from IPCC AR4 closest to satellite observations.

scholarly journals Assessing observed and modelled spatial distributions of ice water path using satellite data

2011 ◽  
Vol 11 (1) ◽  
pp. 375-391 ◽  
Author(s):  
S. Eliasson ◽  
S. A. Buehler ◽  
M. Milz ◽  
P. Eriksson ◽  
V. O. John
Keyword(s):  
Spatial Distribution ◽  
Satellite Data ◽  
Climate Models ◽  
Water Path ◽  
Ice Water Path ◽  
The Individual ◽  
Cloud Ice ◽  
The Tropics ◽  
Ipcc Ar4 ◽  
Ice Water

Abstract. The climate models used in the IPCC AR4 show large differences in monthly mean ice water path (IWP). The most valuable source of information that can be used to potentially constrain the models is global satellite data. The satellite datasets also have large differences. The retrieved IWP depends on the technique used, as retrievals based on different techniques are sensitive to different parts of the cloud column. Building on the foundation of Waliser et al. (2009), this article provides a more comprehensive comparison between satellite datasets. IWP data from the CloudSat cloud profiling radar provide the most advanced dataset on clouds. For all its unmistakable value, CloudSat data are too short and too sparse to assess climatic distributions of IWP, hence the need to also use longer datasets. We evaluate satellite datasets from CloudSat, PATMOS-x, ISCCP, MODIS and MSPPS in terms of monthly mean IWP, in order to determine the differences and relate them to the sensitivity of the instrument used in the retrievals. This information is also used to evaluate the climate models, to the extent that is possible. ISCCP and MSPPS were shown to have comparatively low IWP values. ISCCP shows particularly low values in the tropics, while MSPPS has particularly low values outside the tropics. MODIS and PATMOS-x were in closest agreement with CloudSat in terms of magnitude and spatial distribution, with MODIS being the better of the two. Additionally PATMOS-x and ISCCP, which have a temporal range long enough to capture the inter-annual variability of IWP, are used in conjunction with CloudSat IWP (after removing profiles that contain precipitation) to assess the IWP variability and mean of the climate models. In general there are large discrepancies between the individual climate models, and all of the models show problems in reproducing the observed spatial distribution of cloud-ice. Comparisons consistently showed that ECHAM-5 is probably the GCM from IPCC AR4 closest to satellite observations.

scholarly journals Overview and sample applications of SMILES and Odin-SMR retrievals of upper tropospheric humidity and cloud ice mass

2014 ◽  
Vol 14 (23) ◽  
pp. 12613-12629 ◽  
Author(s):  
P. Eriksson ◽  
B. Rydberg ◽  
H. Sagawa ◽  
M. S. Johnston ◽  
Y. Kasai
Keyword(s):  
Climate Models ◽  
Southern Oscillation ◽  
A Priori ◽  
Global Climate Models ◽  
Monte Carlo Integration ◽  
Data Sets ◽  
Upper Troposphere ◽  
Retrieval Scheme ◽  
Cloud Ice ◽  
Ice Water

Abstract. Retrievals of cloud ice mass and humidity from the Superconducting Submillimeter-Wave Limb-Emission Sounder (SMILES) and the Odin-SMR (Sub-Millimetre Radiometer) limb sounder are presented and example applications of the data are given. SMILES data give an unprecedented view of the diurnal variation of cloud ice mass. Mean regional diurnal cycles are reported and compared to some global climate models. Some improvements in the models regarding diurnal timing and relative amplitude were noted, but the models' mean ice mass around 250 hPa is still low compared to the observations. The influence of the ENSO (El Niño–Southern Oscillation) state on the upper troposphere is demonstrated using 12 years of Odin-SMR data. The same retrieval scheme is applied for both sensors, and gives low systematic differences between the two data sets. A special feature of this Bayesian retrieval scheme, of Monte Carlo integration type, is that values are produced for all measurements but for some atmospheric states retrieved values only reflect a priori assumptions. However, this "all-weather" capability allows a direct statistical comparison to model data, in contrast to many other satellite data sets. Another strength of the retrievals is the detailed treatment of "beam filling" that otherwise would cause large systematic biases for these passive cloud ice mass retrievals. The main retrieval inputs are spectra around 635/525 GHz from tangent altitudes below 8/9 km for SMILES/Odin-SMR, respectively. For both sensors, the data cover the upper troposphere between 30° S and 30° N. Humidity is reported as both relative humidity and volume mixing ratio. The vertical coverage of SMILES is restricted to a single layer, while Odin-SMR gives some profiling capability between 300 and 150 hPa. Ice mass is given as the partial ice water path above 260 hPa, but for Odin-SMR ice water content, estimates are also provided. Besides a smaller contrast between most dry and wet cases, the agreement with Aura MLS (Microwave Limb Sounder) humidity data is good. In terms of tropical mean humidity, all three data sets agree within 3.5 %RHi. Mean ice mass is about a factor of 2 lower compared to CloudSat. This deviation is caused by the fact that different particle size distributions are assumed, combined with saturation and a priori influences in the SMILES and Odin-SMR data.

scholarly journals CloudSat-constrained cloud ice water path and cloud top height retrievals from MHS 157 and 183.3 GHz radiances

2013 ◽  
Vol 6 (5) ◽  
pp. 8187-8233 ◽  
Author(s):  
J. Gong ◽  
D. L. Wu
Keyword(s):  
Climate Models ◽  
Cloud Microphysics ◽  
Water Path ◽  
Ice Cloud ◽  
Cloud Properties ◽  
Ice Water Path ◽  
Satellite Sensors ◽  
Key Variables ◽  
Cloud Ice ◽  
Ice Water

Abstract. Ice water path (IWP) and cloud top height (ht) are two of the key variables to determine cloud radiative and thermodynamical properties in the climate models. Large uncertainty remains among IWP measurements from satellite sensors, in large part due to the assumptions made for cloud microphysics in these retrievals. In this study, we develop a fast algorithm to retrieve IWP from the 157, 183.3 ± 3 and 190.3 GHz radiances of Microwave Humidity Sounder (MHS) such that the MHS cloud ice retrieval is consistent with CloudSat IWP measurements. This retrieval is obtained by constraining the forward models between collocated-and-coincident measurements of CloudSat IWP and MHS cloud-induced radiance depression (Tcir) at these channels. The empirical forward model is represented by a look-up-table (LUT) of Tcir–IWP relationships as a function of ht and frequency channel. With ht simultaneously retrieved, the IWP is found to be more accurate. The useful range of the MHS IWP retrieval is between 0.5 and 10 kg m−2, and agrees well with CloudSat in terms of normalized probability density function (PDF). Compared to the empirical model, current radiative transfer models (RTMs) still have significant uncertainties in characterizing the observed Tcir–IWP relationships. Therefore, the empirical LUT method developed here remains as an effective approach to retrieving ice cloud properties from the MHS-like microwave channels.

scholarly journals The behavior of high-CAPE (convective available potential energy) summer convection in large-domain large-eddy simulations with ICON

2021 ◽  
Vol 21 (6) ◽  
pp. 4285-4318
Author(s):  
Harald Rybka ◽  
Ulrike Burkhardt ◽  
Martin Köhler ◽  
Ioanna Arka ◽  
Luca Bugliaro ◽  
...  
Keyword(s):  
Large Scale ◽  
Data Sets ◽  
Motion Field ◽  
Moist Convection ◽  
Water Path ◽  
Ice Water Path ◽  
Large Eddy ◽  
Deep Moist Convection ◽  
Cloud Ice ◽  
Ice Water

Abstract. Current state-of-the-art regional numerical weather prediction (NWP) models employ kilometer-scale horizontal grid resolutions, thereby simulating convection within the grey zone. Increasing resolution leads to resolving the 3D motion field and has been shown to improve the representation of clouds and precipitation. Using a hectometer-scale model in forecasting mode on a large domain therefore offers a chance to study processes that require the simulation of the 3D motion field at small horizontal scales, such as deep summertime moist convection, a notorious problem in NWP. We use the ICOsahedral Nonhydrostatic weather and climate model in large-eddy simulation mode (ICON-LEM) to simulate deep moist convection and distinguish between scattered, large-scale dynamically forced, and frontal convection. We use different ground- and satellite-based observational data sets, which supply information on ice water content and path, ice cloud cover, and cloud-top height on a similar scale as the simulations, in order to evaluate and constrain our model simulations. We find that the timing and geometric extent of the convectively generated cloud shield agree well with observations, while the lifetime of the convective anvil was, at least in one case, significantly overestimated. Given the large uncertainties of individual ice water path observations, we use a suite of observations in order to better constrain the simulations. ICON-LEM simulates a cloud ice water path that lies between the different observational data sets, but simulations appear to be biased towards a large frozen water path (all frozen hydrometeors). Modifications of parameters within the microphysical scheme have little effect on the bias in the frozen water path and the longevity of the anvil. In particular, one of our convective days appeared to be very sensitive to the initial and boundary conditions, which had a large impact on the convective triggering but little impact on the high frozen water path and long anvil lifetime bias. Based on this limited set of sensitivity experiments, the evolution of locally forced convection appears to depend more on the uncertainty of the large-scale dynamical state based on data assimilation than of microphysical parameters. Overall, we judge ICON-LEM simulations of deep moist convection to be very close to observations regarding the timing, geometrical structure, and cloud ice water path of the convective anvil, but other frozen hydrometeors, in particular graupel, are likely overestimated. Therefore, ICON-LEM supplies important information for weather forecasting and forms a good basis for parameterization development based on physical processes or machine learning.

scholarly journals The behavior of high-CAPE summer convection in large-domain large-eddy simulations with ICON

2020 ◽  
Author(s):  
Harald Rybka ◽  
Ulrike Burkhardt ◽  
Martin Köhler ◽  
Ioanna Arka ◽  
Luca Bugliaro ◽  
...  
Keyword(s):  
Large Scale ◽  
Life Time ◽  
Data Sets ◽  
Motion Field ◽  
Moist Convection ◽  
Water Path ◽  
Ice Water Path ◽  
Deep Moist Convection ◽  
Cloud Ice ◽  
Ice Water

Abstract. Current state of the art regional numerical weather prediction (NWP) models employ kilometre scale horizontal grid resolutions thereby simulating convection within its grey-zone. Increasing resolution leads to resolving the 3D motion field and has been shown to improve the representation of clouds and precipitation. Using a hectometer-scale model in forecasting mode on a large domain therefore offers a chance to study processes that require the simulation of the 3D motion field at small horizontal scales, such as deep summertime moist convection, a notorious problem in NWP. We use the Icosahedral Nonhydrostatic weather and climate model in large-eddy simulation mode (ICON-LEM) to simulate deep moist convection distinguishing between scattered, large scale dynamically forced and frontal convection. We use different ground and satellite based observational data sets, that supply information on ice water content and path, ice cloud cover and cloud top height on a similar scale as the simulations, in order to evaluate and constrain our model simulations. We find that the timing and geometric extent of the convectively generated cloud shield agrees well with observations while the life time of the convective anvil was, at least in one case, significantly overestimated. Given the large uncertainties of individual ice water path observations, we use a suite of observations in order to better constrain the simulations. ICON-LEM simulates cloud ice water path that lies in-between the different observational data sets but simulations appear to be biased towards a large frozen water path (all frozen hydrometeors). The bias in frozen water path and the longevity of the anvil are little affected by modifications of parameters within the microphysical scheme. In particular one of our convective days appeared to be very sensitive to the initial and boundary conditions which had a large impact on the convective triggering, but little impact on the high frozen water path and long anvil life time bias. Based on this limited set of sensitivity experiments, the evolution of locally forced convection appears to depend more on the uncertainty of the large-scale dynamical state based on data assimilation than of microphysical parameters. Overall, we judge ICON-LEM simulations of deep moist convection to be very close to observations regarding timing, geometrical structure and cloud ice water path of the convective anvil, but other frozen hydrometeors, in particular graupel, are likely overestimated. Therefore, ICON-LEM supplies important information for weather forecasting and forms a good basis for parameterization development based on physical processes or machine learning.

scholarly journals CloudSat-constrained cloud ice water path and cloud top height retrievals from MHS 157 and 183.3 GHz radiances

2014 ◽  
Vol 7 (6) ◽  
pp. 1873-1890 ◽  
Author(s):  
J. Gong ◽  
D. L. Wu
Keyword(s):  
Climate Models ◽  
Cloud Microphysics ◽  
Water Path ◽  
Ice Cloud ◽  
Cloud Properties ◽  
Ice Water Path ◽  
Satellite Sensors ◽  
Key Variables ◽  
Cloud Ice ◽  
Ice Water

Abstract. Ice water path (IWP) and cloud top height (ht) are two of the key variables in determining cloud radiative and thermodynamical properties in climate models. Large uncertainty remains among IWP measurements from satellite sensors, in large part due to the assumptions made for cloud microphysics in these retrievals. In this study, we develop a fast algorithm to retrieve IWP from the 157, 183.3 ± 3 and 190.3 GHz radiances of the Microwave Humidity Sounder (MHS) such that the MHS cloud ice retrieval is consistent with CloudSat IWP measurements. This retrieval is obtained by constraining the empirical forward models between collocated and coincident measurements of CloudSat IWP and MHS cloud-induced radiance depression (Tcir) at these channels. The empirical forward model is represented by a look-up table (LUT) of Tcir–IWP relationships as a function of ht and the frequency channel. With ht simultaneously retrieved, the IWP is found to be more accurate. The useful range of the MHS IWP retrieval is between 0.5 and 10 kg m−2, and agrees well with CloudSat in terms of the normalized probability density function (PDF). Compared to the empirical model, current operational radiative transfer models (RTMs) still have significant uncertainties in characterizing the observed Tcir–IWP relationships. Therefore, the empirical LUT method developed here remains an effective approach to retrieving ice cloud properties from the MHS-like microwave channels.

scholarly journals Do galactic bars depend on environment?: an information theoretic analysis of Galaxy Zoo 2

2020 ◽  
Vol 501 (1) ◽  
pp. 994-1001
Author(s):  
Suman Sarkar ◽  
Biswajit Pandey ◽  
Snehasish Bhattacharjee
Keyword(s):  
Spatial Distribution ◽  
Mutual Information ◽  
Local Density ◽  
Statistical Significance ◽  
Distribution Functions ◽  
Cumulative Distribution ◽  
Host Galaxy ◽  
Data Sets ◽  
Data Set ◽  
Information Theoretic

ABSTRACT We use an information theoretic framework to analyse data from the Galaxy Zoo 2 project and study if there are any statistically significant correlations between the presence of bars in spiral galaxies and their environment. We measure the mutual information between the barredness of galaxies and their environments in a volume limited sample (Mr ≤ −21) and compare it with the same in data sets where (i) the bar/unbar classifications are randomized and (ii) the spatial distribution of galaxies are shuffled on different length scales. We assess the statistical significance of the differences in the mutual information using a t-test and find that both randomization of morphological classifications and shuffling of spatial distribution do not alter the mutual information in a statistically significant way. The non-zero mutual information between the barredness and environment arises due to the finite and discrete nature of the data set that can be entirely explained by mock Poisson distributions. We also separately compare the cumulative distribution functions of the barred and unbarred galaxies as a function of their local density. Using a Kolmogorov–Smirnov test, we find that the null hypothesis cannot be rejected even at $75{{\ \rm per\ cent}}$ confidence level. Our analysis indicates that environments do not play a significant role in the formation of a bar, which is largely determined by the internal processes of the host galaxy.

Satellite Remote Sensing of Cirrus

Cirrus ◽  
2002 ◽  
Author(s):  
Patrick Minnis
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Climate Models ◽  
Cirrus Clouds ◽  
Data Sets ◽  
Visible Radiation ◽  
Accurate Identification ◽  
Spatial And Temporal Scales ◽  
Sensing Systems ◽  
Particle Size And Shape

The determination of cirrus properties over large spatial and temporal scales will, in most instances, require the use of satellite data. Global coverage at resolutions as fine as several meters are attainable with instruments on Landsat, and temporal coverage at 1-min intervals is now available with the latest Geostationary Operational Environmental Satellite (GOES) imagers. Extracting information about cirrus clouds from these satellite data sets is often difficult because of variations in background, similarities to other cloud types, and the frequently semitransparent nature of cirrus clouds. From the surface, cirrus clouds are readily discerned by the human observer via the patterns of scattered visible radiation from the sun, moon, and stars. The relatively uniform background presented by the sky facilitates cloud detection and the familiar textures, structures, and apparent altitude of cirrus distinguish it from other cloud types. From satellites, cirrus can also be detected from scattered visible radiation, but the demands of accurate identification for different surface backgrounds over the entire diurnal cycle and quantification of the cirrus properties require the analysis of radiances scattered or emitted over a wide range of the electromagnetic spectrum. Many of these spectra and high-resolution satellite data can be used to understand certain aspects of cirrus clouds in particular situations. Intensive study of well-measured cases can yield a wealth of information about cirrus properties on fine scales (e.g., Minnis et al. 1990; Westphal et al. 1996). Production of a global climatology of cirrus clouds, however, requires compromises in spatial, temporal, and spectral coverage (e.g., Schiffer and Rossow 1983). This chapter summarizes both the state of the art and the potential for future passive remote sensing systems to aid the understanding of cirrus processes and to acquire sufficient statistics for constraining and refining weather and climate models. Theoretically, many different aspects of cirrus can be determined from passive sensing systems. A limited number of quantities are the focus of most efforts to describe cirrus clouds. These include the areal coverage, top and base altitude or pressure, thickness, top and base temperatures, optical depth, effective particle size and shape, vertical ice water path, and size, shape and spacing of the cloud cells.

scholarly journals Monofractal or multifractal: a case study of spatial distribution of mining-induced seismic activity

1994 ◽  
Vol 1 (2/3) ◽  
pp. 182-190 ◽  
Author(s):  
M. Eneva
Keyword(s):  
Spatial Distribution ◽  
Seismic Activity ◽  
Real Data ◽  
Data Sets ◽  
Point Sets ◽  
Data Set ◽  
Limited Size ◽  
The Real ◽  
Induced Seismic Activity ◽  
Generalized Correlation

Abstract. Using finite data sets and limited size of study volumes may result in significant spurious effects when estimating the scaling properties of various physical processes. These effects are examined with an example featuring the spatial distribution of induced seismic activity in Creighton Mine (northern Ontario, Canada). The events studied in the present work occurred during a three-month period, March-May 1992, within a volume of approximate size 400 x 400 x 180 m3. Two sets of microearthquake locations are studied: Data Set 1 (14,338 events) and Data Set 2 (1654 events). Data Set 1 includes the more accurately located events and amounts to about 30 per cent of all recorded data. Data Set 2 represents a portion of the first data set that is formed by the most accurately located and the strongest microearthquakes. The spatial distribution of events in the two data sets is examined for scaling behaviour using the method of generalized correlation integrals featuring various moments q. From these, generalized correlation dimensions are estimated using the slope method. Similar estimates are made for randomly generated point sets using the same numbers of events and the same study volumes as for the real data. Uniform and monofractal random distributions are used for these simulations. In addition, samples from the real data are randomly extracted and the dimension spectra for these are examined as well. The spectra for the uniform and monofractal random generations show spurious multifractality due only to the use of finite numbers of data points and limited size of study volume. Comparing these with the spectra of dimensions for Data Set 1 and Data Set 2 allows us to estimate the bias likely to be present in the estimates for the real data. The strong multifractality suggested by the spectrum for Data Set 2 appears to be largely spurious; the spatial distribution, while different from uniform, could originate from a monofractal process. The spatial distribution of microearthquakes in Data Set 1 is either monofractal as well, or only weakly multifractal. In all similar studies, comparisons of result from real data and simulated point sets may help distinguish between genuine and artificial multifractality, without necessarily resorting to large number of data.

scholarly journals Estimating observation and model error variances using multiple data sets

2018 ◽  
Vol 11 (7) ◽  
pp. 4239-4260 ◽  
Author(s):  
Richard Anthes ◽  
Therese Rieckh
Keyword(s):  
Error Variance ◽  
Specific Humidity ◽  
Data Sets ◽  
Data Set ◽  
Multiple Data ◽  
Gfs Model ◽  
Multiple Data Sets ◽  
Using Data ◽  
The Tropics ◽  
Estimated Error

Abstract. In this paper we show how multiple data sets, including observations and models, can be combined using the “three-cornered hat” (3CH) method to estimate vertical profiles of the errors of each system. Using data from 2007, we estimate the error variances of radio occultation (RO), radiosondes, ERA-Interim, and Global Forecast System (GFS) model data sets at four radiosonde locations in the tropics and subtropics. A key assumption is the neglect of error covariances among the different data sets, and we examine the consequences of this assumption on the resulting error estimates. Our results show that different combinations of the four data sets yield similar relative and specific humidity, temperature, and refractivity error variance profiles at the four stations, and these estimates are consistent with previous estimates where available. These results thus indicate that the correlations of the errors among all data sets are small and the 3CH method yields realistic error variance profiles. The estimated error variances of the ERA-Interim data set are smallest, a reasonable result considering the excellent model and data assimilation system and assimilation of high-quality observations. For the four locations studied, RO has smaller error variances than radiosondes, in agreement with previous studies. Part of the larger error variance of the radiosondes is associated with representativeness differences because radiosondes are point measurements, while the other data sets represent horizontal averages over scales of ∼ 100 km.

Sign in / Sign up

Export Citation Format

Share Document