scholarly journals Towards All-sky Assimilation of Microwave Temperature Sounding Channels in Environment Canada’s Global Deterministic Weather Prediction System

2021 ◽  
Author(s):  
Maziar Bani Shahabadi ◽  
Mark Buehner
Keyword(s):  
Standard Deviation ◽  
Weather Prediction ◽  
Lower Troposphere ◽  
Forecast Model ◽  
Specific Humidity ◽  
Horizontal Wind ◽  
Observation Error ◽  
Horizontal Wind Speed ◽  
Observation Operator ◽  
Background State

AbstractThe all-sky assimilation of radiances from microwave instruments is developed in the 4D-EnVar analysis system at Environment and Climate Change Canada (ECCC). Assimilation of cloud-affected radiances from Advanced Microwave Sounding Unit A (AMSUA) temperature sounding channels 4 and 5 for non-precipitating scenes over the ocean surface is the focus of this study. Cloud-affected radiances are discarded in the ECCC operational data assimilation system due to the limitations of forecast model physics, radiative transfer models, and the strong non-linearity of the observation operator. In addition to using symmetric estimate of innovation standard deviation for quality control, a state-dependent observation error inflation is employed at the analysis stage. The background state clouds are scaled by a factor of 0.5 to compensate for a systematic overestimation by the forecast model, before being used in the observation operator. The changes in the fit of the background state to observations show mixed results. The number of AMSUA channels 4 and 5 assimilated observations in the all-sky experiment is 5-12% higher than in the operational system. The all-sky approach improves temperature analysis when verified against ECMWF operational analysis in the areas where the extra cloud-affected observations were assimilated. Statistically significant reductions in error standard deviation by 1-4% for the analysis and forecasts of temperature, specific humidity, and horizontal wind speed up to maximum 4 days were achieved in the all-sky experiment in the lower troposphere. These improvements result mainly from the use of cloud information for computing the observation-minus-background departures. The operational implementation of all-sky assimilation is planned for Fall 2021.

scholarly journals Global 3D Features of Error Variances of GPS Radio Occultation and Radiosonde Observations

2020 ◽  
Vol 13 (1) ◽  
pp. 1
Author(s):  
Xu Xu ◽  
Xiaolei Zou
Keyword(s):  
Radio Occultation ◽  
Weather Prediction ◽  
Lower Troposphere ◽  
Error Variance ◽  
Specific Humidity ◽  
Temperature Error ◽  
Observation Error ◽  
Bending Angle ◽  
Gps Radio Occultation ◽  
Low Latitudes

Global Positioning System (GPS) radio occultation (RO) and radiosonde (RS) observations are two major types of observations assimilated in numerical weather prediction (NWP) systems. Observation error variances are required input that determines the weightings given to observations in data assimilation. This study estimates the error variances of global GPS RO refractivity and bending angle and RS temperature and humidity observations at 521 selected RS stations using the three-cornered hat method with additional ERA-Interim reanalysis and Global Forecast System forecast data available from 1 January 2016 to 31 August 2019. The global distributions, of both RO and RS observation error variances, are analyzed in terms of vertical and latitudinal variations. Error variances of RO refractivity and bending angle and RS specific humidity in the lower troposphere, such as at 850 hPa (3.5 km impact height for the bending angle), all increase with decreasing latitude. The error variances of RO refractivity and bending angle and RS specific humidity can reach about 30 N-unit2, 3 × 10−6 rad2, and 2 (g kg−1)2, respectively. There is also a good symmetry of the error variances of both RO refractivity and bending angle with respect to the equator between the Northern and Southern Hemispheres at all vertical levels. In this study, we provide the mean error variances of refractivity and bending angle in every 5°-latitude band between the equator and 60°N, as well as every interval of 10 hPa pressure or 0.2 km impact height. The RS temperature error variance distribution differs from those of refractivity, bending angle, and humidity, which, at low latitudes, are smaller (less than 1 K2) than those in the midlatitudes (more than 3 K2). In the midlatitudes, the RS temperature error variances in North America are larger than those in East Asia and Europe, which may arise from different radiosonde types among the above three regions.

scholarly journals Challenges of Increased Resolution for the Local Ensemble Tangent Linear Model

2020 ◽  
Vol 148 (6) ◽  
pp. 2549-2566
Author(s):  
Douglas R. Allen ◽  
Sergey Frolov ◽  
Rolf Langland ◽  
Craig H. Bishop ◽  
Karl W. Hoppel ◽  
...  
Keyword(s):  
Linear Model ◽  
Fast Moving ◽  
Weather Prediction ◽  
Lower Troposphere ◽  
Forecast Model ◽  
Nonlocal Operators ◽  
Upper Troposphere ◽  
Global Environmental ◽  
Nonlinear Normal ◽  
Normal Mode Initialization

Abstract An ensemble-based linearized forecast model has been developed for data assimilation applications for numerical weather prediction. Previous studies applied this local ensemble tangent linear model (LETLM) to various models, from simple one-dimensional models to a low-resolution (~2.5°) version of the Navy Global Environmental Model (NAVGEM) atmospheric forecast model. This paper applies the LETLM to NAVGEM at higher resolution (~1°), which required overcoming challenges including 1) balancing the computational stencil size with the ensemble size, and 2) propagating fast-moving gravity modes in the upper atmosphere. The first challenge is addressed by introducing a modified local influence volume, introducing computations on a thin grid, and using smaller time steps. The second challenge is addressed by applying nonlinear normal mode initialization, which damps spurious fast-moving modes and improves the LETLM errors above ~100 hPa. Compared to a semi-Lagrangian tangent linear model (TLM), the LETLM has superior skill in the lower troposphere (below 700 hPa), which is attributed to better representation of moist physics in the LETLM. The LETLM skill slightly lags in the upper troposphere and stratosphere (700–2 hPa), which is attributed to nonlocal aspects of the TLM including spectral operators converting from winds to vorticity and divergence. Several ways forward are suggested, including integrating the LETLM in a hybrid 4D variational solver for a realistic atmosphere, combining a physics LETLM with a conventional TLM for the dynamics, and separating the LETLM into a sequence of local and nonlocal operators.

scholarly journals Aircraft validation of Aura Tropospheric Emission Spectrometer retrievals of HDO / H<sub>2</sub>O

2014 ◽  
Vol 7 (9) ◽  
pp. 3127-3138 ◽  
Author(s):  
R. L. Herman ◽  
J. E. Cherry ◽  
J. Young ◽  
J. M. Welker ◽  
D. Noone ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Water Vapor ◽  
Standard Deviation ◽  
Lower Troposphere ◽  
Temporal Variations ◽  
Observation Error ◽  
Free Troposphere ◽  
Data Set ◽  
Airborne Measurements

Abstract. The EOS (Earth Observing System) Aura Tropospheric Emission Spectrometer (TES) retrieves the atmospheric HDO / H2O ratio in the mid-to-lower troposphere as well as the planetary boundary layer. TES observations of water vapor and the HDO isotopologue have been compared with nearly coincident in situ airborne measurements for direct validation of the TES products. The field measurements were made with a commercially available Picarro L1115-i isotopic water analyzer on aircraft over the Alaskan interior boreal forest during the three summers of 2011 to 2013. TES special observations were utilized in these comparisons. The TES averaging kernels and a priori constraints have been applied to the in situ data, using version 5 (V005) of the TES data. TES calculated errors are compared with the standard deviation (1σ) of scan-to-scan variability to check consistency with the TES observation error. Spatial and temporal variations are assessed from the in situ aircraft measurements. It is found that the standard deviation of scan-to-scan variability of TES δD is ±34.1‰ in the boundary layer and ± 26.5‰ in the free troposphere. This scan-to-scan variability is consistent with the TES estimated error (observation error) of 10–18‰ after accounting for the atmospheric variations along the TES track of ±16‰ in the boundary layer, increasing to ±30‰ in the free troposphere observed by the aircraft in situ measurements. We estimate that TES V005 δD is biased high by an amount that decreases with pressure: approximately +123‰ at 1000 hPa, +98‰ in the boundary layer and +37‰ in the free troposphere. The uncertainty in this bias estimate is ±20‰. A correction for this bias has been applied to the TES HDO Lite Product data set. After bias correction, we show that TES has accurate sensitivity to water vapor isotopologues in the boundary layer.

scholarly journals Asymmetricity of ground-based GPS slant delay data

2007 ◽  
Vol 7 (12) ◽  
pp. 3143-3151 ◽  
Author(s):  
R. Eresmaa ◽  
H. Järvinen ◽  
S. Niemelä ◽  
K. Salonen
Keyword(s):  
Standard Deviation ◽  
Global Positioning System ◽  
Weather Prediction ◽  
Small Scale ◽  
Tropospheric Delay ◽  
Observation Error ◽  
Positioning System ◽  
Global Positioning ◽  
Nwp Model ◽  
Delay Component

Abstract. The ground-based measurements of the Global Positioning System (GPS) allow estimation of the tropospheric delay along the slanted signal paths through the atmosphere. The meteorological exploitation of such slant delay (SD) observations relies on the hypothesis of azimuthal asymmetry of the information content. This article addresses the validity of the hypothesis. A new concept of asymmetricity is introduced for studying the SD observations and their model counterparts. The asymmetricity is defined as the ratio of the absolute asymmetric delay component to total SD. The model counterparts are determined from 3-h forecasts of a numerical weather prediction (NWP) model, run with four different horizontal resolutions. The SD observations are compared with their model counterparts with emphasis on cases of high asymmetricity in order to see whether the observed asymmetry is a real atmospheric signature. The asymmetricity is found to be of the order of a few parts per thousand. Thus, the asymmetric delay component barely exceeds the assumed standard deviation of the SD observation error. However, the observed asymmetric delay components show a statistically significant meteorological signal. Benefit of the asymmetric SD observations is therefore expected to be taken in future, when NWP systems will explicitly represent the small-scale atmospheric features revealed by the SD observations.

Climatology of Non-Gaussian Atmospheric Statistics

2013 ◽  
Vol 26 (3) ◽  
pp. 1063-1083 ◽  
Author(s):  
Maxime Perron ◽  
Philip Sura
Keyword(s):  
Grid Point ◽  
Meridional Wind ◽  
Relative Vorticity ◽  
Statistical Moments ◽  
Specific Humidity ◽  
Horizontal Wind ◽  
Horizontal Wind Speed ◽  
Daily Data ◽  
Non Gaussian ◽  
Skewness And Kurtosis

Abstract A common assumption in the earth sciences is the Gaussianity of data over time. However, several independent studies in the past few decades have shown this assumption to be mostly false. To be able to study non-Gaussian climate statistics, one must first compile a systematic climatology of the higher statistical moments (skewness and kurtosis; the third and fourth central statistical moments, respectively). Sixty-two years of daily data from the NCEP–NCAR Reanalysis I project are analyzed. The skewness and kurtosis of the data are found at each spatial grid point for the entire time domain. Nine atmospheric variables were chosen for their physical and dynamical relevance in the climate system: geopotential height, relative vorticity, quasigeostrophic potential vorticity, zonal wind, meridional wind, horizontal wind speed, vertical velocity in pressure coordinates, air temperature, and specific humidity. For each variable, plots of significant global skewness and kurtosis are shown for December–February and June–August at a specified pressure level. Additionally, the statistical moments are then zonally averaged to show the vertical dependence of the non-Gaussian statistics. This is a more comprehensive look at non-Gaussian atmospheric statistics than has been taken in previous studies on this topic.

scholarly journals On the Formulation and Universality of Monin–Obukhov Similarity Functions for Mean Gradients and Standard Deviations in the Unstable Surface Layer: Results from Surface-Layer-Resolving Large-Eddy Simulations

2017 ◽  
Vol 74 (4) ◽  
pp. 989-1010 ◽  
Author(s):  
Björn Maronga ◽  
Joachim Reuder
Keyword(s):  
Surface Layer ◽  
Standard Deviation ◽  
Large Eddy Simulations ◽  
Specific Humidity ◽  
Horizontal Wind ◽  
Convective Conditions ◽  
Similarity Functions ◽  
Large Eddy ◽  
The Mean ◽  
Standard Deviations

Abstract Surface-layer-resolving large-eddy simulations (LESs) of free-convective to near-neutral boundary layers are used to study Monin–Obukhov similarity theory (MOST) functions. The LES dataset, previously used for the analysis of MOST relationships for structure parameters, is extended for the mean vertical gradients and standard deviations of potential temperature, specific humidity, and wind. Also, local-free-convection (LFC) similarity is studied. The LES data suggest that the MOST functions for mean gradients are universal and unique. The data for the mean gradient of the horizontal wind display significant scatter, while the gradients of temperature and humidity vary considerably less. The LES results suggest that this scatter is mostly related to a transition from MOST to LFC scaling when approaching free-convective conditions and that it is associated with a change of the slope of the similarity functions toward the expected value from LFC scaling. Overall, the data show slightly, but consistent, steeper slopes of the similarity functions than suggested in literature. The MOST functions for standard deviations appear to be unique and universal when the entrainment from the free atmosphere into the boundary layer is sufficiently small. If entrainment becomes significant, however, we find that the standard deviation of humidity no longer follows MOST. Under free-convective conditions, the similarity functions should reduce to universal constants (LFC scaling). This is supported by the LES data, showing only little scatter, but displaying a systematic height dependence of these constants. Like for MOST, the LFC similarity constant for the standard deviation of specific humidity becomes nonuniversal when the entrainment of dry air reaches significant levels.

System of Multigrid NLS-4DVar Data Assimilation for Numerical Weather Prediction (SNAP)：System Formulation and Preliminary Evaluation

2020 ◽  
Author(s):  
Hongqin Zhang ◽  
Xiangjun Tian
Keyword(s):  
Data Assimilation ◽  
Data Processing ◽  
Numerical Weather Prediction ◽  
Computational Efficiency ◽  
Weather Prediction ◽  
Forecast Model ◽  
Regional Model ◽  
Numerical Weather ◽  
Observation Operator ◽  
Numerical Forecast

&lt;p class=&quot;a&quot;&gt;&lt;span lang=&quot;EN-US&quot;&gt;The system of multigrid NLS-4DVar data assimilation for Numerical Weather Prediction (SNAP) is established, building upon the multigrid NLS-4DVar assimilation scheme, the operational Gridpoint Statistical Interpolation (GSI)-based data-processing and observation operator and widely used numerical forecast model WRF (easily replaced by others global/regional model). The multigrid assimilation framework can adequately correct errors from large to small scales to achieve higher assimilation accuracy. Meanwhile, the multigrid strategy can accelerate iteration solution improving the computational efficiency. NLS-4DVar, as an advanced 4DEnVar method, employs the Gauss-Newton iterative method to handle the nonlinear of the 4DVar cost function and provides the flow-dependent background error covariance, which both contribute to the assimilation accuracy. The efficient local correlation matrix decomposition approach and its application in the fast localization scheme of NLS-4DVar and obviating the need of the tangent linear and adjoint model further improve the computational efficiency. The numerical forecast model of SNAP is any optional global/regional model, which makes the application of SNAP very flexible. The analysis variables of SNAP are rather the model state variables than the control variables adopted in the usual 4DVar system. The data-processing and observation operator modules are used from the National Centers for Environmental Prediction (NCEP) operational GSI analysis system, prominent in the various observation operators and the ability to assimilate multi-source observations. Currently, we have achieved the assimilation of conventional observations and we will continue to improve the assimilation of radar and satellite observations in the future. The performance of SNAP was investigated assimilating conventional observations used for the generation of the operational global atmospheric reanalysis product (CRA-40) by the National Meteorological Information Center of China Meteorological Administration. Cyclic assimilation experiments with two windows, which is 6-h for each window, are designed. The results of numerical experiments show that SNAP can absorb observations, improve initial field, and then improve precipitation forecast. &lt;/span&gt;&lt;/p&gt;

scholarly journals Evaluating the lower-tropospheric COSMIC GPS radio occultation sounding quality over the Arctic

2018 ◽  
Vol 11 (4) ◽  
pp. 2051-2066 ◽  
Author(s):  
Xiao Yu ◽  
Feiqin Xie ◽  
Chi O. Ao
Keyword(s):  
Data Processing ◽  
Radio Occultation ◽  
Weather Prediction ◽  
Lower Troposphere ◽  
Parametric Uncertainty ◽  
Ocean Surface ◽  
Specific Humidity ◽  
The Arctic ◽  
Near Surface ◽  
Active Research

Abstract. Lower-tropospheric moisture and temperature measurements are crucial for understanding weather prediction and climate change. Global Positioning System radio occultation (GPS RO) has been demonstrated as a high-quality observation technique with high vertical resolution and sub-kelvin temperature precision from the upper troposphere to the stratosphere. In the tropical lower troposphere, particularly the lowest 2 km, the quality of RO retrievals is known to be degraded and is a topic of active research. However, it is not clear whether similar problems exist at high latitudes, particularly over the Arctic, which is characterized by smooth ocean surface and often negligible moisture in the atmosphere. In this study, 3-year (2008–2010) GPS RO soundings from COSMIC (Constellation Observing System for Meteorology, Ionosphere, and Climate) over the Arctic (65–90° N) show uniform spatial sampling with average penetration depth within 300 m above the ocean surface. Over 70 % of RO soundings penetrate deep into the lowest 300 m of the troposphere in all non-summer seasons. However, the fraction of such deeply penetrating profiles reduces to only about 50–60 % in summer, when near-surface moisture and its variation increase. Both structural and parametric uncertainties of GPS RO soundings were also analyzed. The structural uncertainty (due to different data processing approaches) is estimated to be within  ∼  0.07 % in refractivity,  ∼  0.72 K in temperature, and  ∼  0.05 g kg−1 in specific humidity below 10 km, which is derived by comparing RO retrievals from two independent data processing centers. The parametric uncertainty (internal uncertainty of RO sounding) is quantified by comparing GPS RO with near-coincident radiosonde and European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim profiles. A systematic negative bias up to  ∼  1 % in refractivity below 2 km is only seen in the summer, which confirms the moisture impact on GPS RO quality.

scholarly journals Observation Error Statistics for Doppler Radar Radial Wind Superobservations Assimilated into the DWD COSMO-KENDA System

2019 ◽  
Vol 147 (9) ◽  
pp. 3351-3364 ◽  
Author(s):  
J. A. Waller ◽  
E. Bauernschubert ◽  
S. L. Dance ◽  
N. K. Nichols ◽  
R. Potthast ◽  
...  
Keyword(s):  
Doppler Radar ◽  
Weather Prediction ◽  
Processing System ◽  
Separation Distance ◽  
Observation Error ◽  
Error Statistics ◽  
Observation Operator ◽  
Uncertainty Estimates ◽  
Standard Deviations ◽  
The Impact

AbstractCurrently in operational numerical weather prediction (NWP) the density of high-resolution observations, such as Doppler radar radial winds (DRWs), is severely reduced in part to avoid violating the assumption of uncorrelated observation errors. To improve the quantity of observations used and the impact that they have on the forecast requires an accurate specification of the observation uncertainties. Observation uncertainties can be estimated using a simple diagnostic that utilizes the statistical averages of observation-minus-background and observation-minus-analysis residuals. We are the first to use a modified form of the diagnostic to estimate spatial correlations for observations used in an operational ensemble data assimilation system. The uncertainties for DRW superobservations assimilated into the Deutscher Wetterdienst convection-permitting NWP model are estimated and compared to previous uncertainty estimates for DRWs. The new results show that most diagnosed standard deviations are smaller than those used in the assimilation, hence, it may be feasible to assimilate DRWs using reduced error standard deviations. However, some of the estimated standard deviations are considerably larger than those used in the assimilation; these large errors highlight areas where the observation processing system may be improved. The error correlation length scales are larger than the observation separation distance and influenced by both the superobbing procedure and observation operator. This is supported by comparing these results to our previous study using Met Office data. Our results suggest that DRW error correlations may be reduced by improving the superobbing procedure and observation operator; however, any remaining correlations should be accounted for in the assimilation.

scholarly journals Tropospheric Water Vapor Transport as Determined from Airborne Lidar Measurements

2010 ◽  
Vol 27 (12) ◽  
pp. 2017-2030 ◽  
Author(s):  
Andreas Schäfler ◽  
Andreas Dörnbrack ◽  
Christoph Kiemle ◽  
Stephan Rahm ◽  
Martin Wirth
Keyword(s):  
Water Vapor ◽  
Wind Speed ◽  
Moisture Transport ◽  
Lower Troposphere ◽  
Airborne Lidar ◽  
Vapor Transport ◽  
Specific Humidity ◽  
Water Vapor Transport ◽  
Horizontal Wind ◽  
Warm Sector

Abstract The first collocated measurements during THORPEX (The Observing System Research and Predictability Experiment) regional campaign in Europe in 2007 were performed by a novel four-wavelength differential absorption lidar and a scanning 2-μm Doppler wind lidar on board the research aircraft Falcon of the Deutsches Zentrum für Luft- und Raumfahrt (DLR). One mission that was characterized by exceptionally high data coverage (47% for the specific humidity q and 63% for the horizontal wind speed υh) was selected to calculate the advective transport of atmospheric moisture qυh along a 1600-km section in the warm sector of an extratropical cyclone. The observations are compared with special 1-hourly model data calculated by the ECMWF integrated forecast system. Along the cross section, the model underestimates the wind speed on average by −2.8% (−0.6 m s−1) and overestimates the moisture at dry layers and in the boundary layer, which results in a wet bias of 17.1% (0.2 g kg−1). Nevertheless, the ECMWF model reproduces quantitatively the horizontally averaged moisture transport in the warm sector. There, the superposition of high low-level humidity and the increasing wind velocities with height resulted in a deep tropospheric layer of enhanced water vapor transport qυh. The observed moisture transport is variable and possesses a maximum of qυh = 130 g kg−1 m s−1 in the lower troposphere. The pathways of the moisture transport from southwest via several branches of different geographical origin are identified by Lagrangian trajectories and by high values of the vertically averaged tropospheric moisture transport.

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