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NASA NTRS2008

Evaluation of Cloud Physical Properties of ECMWF Analysis and Re-Analysis (ERA-40 and ERA Interim) against CERES Tropical Deep Convective Cloud Object Observations

This study presents an approach that converts the vertical profiles of grid-averaged cloud properties from large-scale models to probability density functions (pdfs) of subgrid-cell cloud physical properties measured at satellite footprints. Cloud physical and radiative properties, rather than just cloud and precipitation occurrences, of assimilated cloud systems by the European Center for Medium-range Weather Forecasts (ECMWF) operational analysis (EOA) and ECMWF Re-Analyses (ERA-40 and ERA Interim) are validated against those obtained from Earth Observing System satellite cloud object data for January-August 1998 and March 2000 periods. These properties include ice water path (IWP), cloud-top height and temperature, cloud optical depth and solar and infrared radiative fluxes. Each cloud object, a contiguous region with similar cloud physical properties, is temporally and spatially matched with EOA and ERA-40 data. Results indicate that most pdfs of EOA and ERA-40 cloud physical and radiative properties agree with those of satellite observations of the tropical deep convective cloud-object type for the January-August 1998 period. There are, however, significant discrepancies in selected ranges of the cloud property pdfs such as the upper range of EOA cloud top height. A major discrepancy is that the dependence of the pdfs on the cloud object size for both EOA and ERA-40 is not as strong as in the observations. Modifications to the cloud parameterization in ECMWF that occurred in October 1999 eliminate the clouds near the tropopause but shift power of the pdf to lower cloud-top heights and greatly reduce the ranges of IWP and cloud optical depth pdfs. These features persist in ERA-40 due to the use of the same cloud parameterizations. The downgrade of data assimilation technique and the lack of snow water content information in ERA-40, not the coarser horizontal grid resolution, are also responsible for the disagreements with observed pdfs of cloud physical properties although the detection rates of cloud object occurrence are improved for small size categories. A possible improvement to the convective parameterization is to introduce a stronger dependence of updraft penetration heights with grid-cell dynamics. These conclusions will be rechecked using the ERA Interim data, due to recent changes in the ECMWF convective parameterization (Bechtold et al. 2004, 2008). Results from the ERA Interim will be presented at the meeting.

Xu, Kuan-Man↗

NASA NTRS2006

Using Multi-scale Modeling System to Study the Interactions between Clouds, Precipitation, Aerosols, Radiation and Land Surface

Numerical cloud models, which are based the non-hydrostatic equations of motion, have been extensively applied to cloud-scale and mesoscale processes during the past four decades. Because cloud-scale dynamics are treated explicitly, uncertainties stemming from convection that have to be parameterized in (hydrostatic) large-scale models are obviated, or at least mitigated, in cloud models. Global models will use the non-hydrostatic framework when their horizontal resolution becomes about 10 km, the theoretical limit for the hydrostatic approximation. This juncture will be reached one to two decades from now. Over the past generation, voluminous datasets on atmospheric convection have been accumulated from radar, instrumented aircraft, satellites, and rawinsonde measurements in field campaigns, enabling the detailed evaluation of models. Improved numerical methods have resulted in more accurate and efficient dynamical cores in models. Improvements have been made in the parameterizations of microphysical processes, radiation, boundary-layer effects, and turbulence; however, microphysical parameterizations remain a major source of uncertainty in all classes of atmospheric models. In recent years, exponentially increasing computer power has extended cloud-resolving-model integrations from hours to months, the number of computational grid points from less than a thousand to close to ten million. Three-dimensional models are now more prevalent. Much attention is devoted to precipitating cloud systems where the crucial 1-km scales are resolved in horizontal domains as large as 10,000 km in two-dimensions, and 1,000 x 1,000 sq km in three-dimensions. Cloud models now provide statistical information useful for developing more realistic physically based parameterizations for climate models and numerical weather prediction models. It is also expected that NWP and mesoscale model can be run in grid size similar to cloud resolving model through nesting technique. A review of developments, improvements and applications of cloud models (GCE and WRF) at Goddard will be presented in this talk. In particular, a new approach to using multi-scale modeling system to study the interactions between clouds, precipitation, aerosols and land will be presented.

Tao, Wei-Kuo↗

NASA NTRS2008

The Super Tuesday Outbreak: Forecast Sensitivities to Single-Moment Microphysics Schemes

Forecast precipitation and radar characteristics are used by operational centers to guide the issuance of advisory products. As operational numerical weather prediction is performed at increasingly finer spatial resolution, convective precipitation traditionally represented by sub-grid scale parameterization schemes is now being determined explicitly through single- or multi-moment bulk water microphysics routines. Gains in forecasting skill are expected through improved simulation of clouds and their microphysical processes. High resolution model grids and advanced parameterizations are now available through steady increases in computer resources. As with any parameterization, their reliability must be measured through performance metrics, with errors noted and targeted for improvement. Furthermore, the use of these schemes within an operational framework requires an understanding of limitations and an estimate of biases so that forecasters and model development teams can be aware of potential errors. The National Severe Storms Laboratory (NSSL) Spring Experiments have produced daily, high resolution forecasts used to evaluate forecast skill among an ensemble with varied physical parameterizations and data assimilation techniques. In this research, high resolution forecasts of the 5-6 February 2008 Super Tuesday Outbreak are replicated using the NSSL configuration in order to evaluate two components of simulated convection on a large domain: sensitivities of quantitative precipitation forecasts to assumptions within a single-moment bulk water microphysics scheme, and to determine if these schemes accurately depict the reflectivity characteristics of well-simulated, organized, cold frontal convection. As radar returns are sensitive to the amount of hydrometeor mass and the distribution of mass among variably sized targets, radar comparisons may guide potential improvements to a single-moment scheme. In addition, object-based verification metrics are evaluated for their utility in gauging model performance and QPF variability.

Molthan, Andrew L.↗

NASA NTRS2012

A Comparison of Perturbed Initial Conditions and Multiphysics Ensembles in a Severe Weather Episode in Spain

Ensembles of numerical model forecasts are of interest to operational early warning forecasters as the spread of the ensemble provides an indication of the uncertainty of the alerts, and the mean value is deemed to outperform the forecasts of the individual models. This paper explores two ensembles on a severe weather episode in Spain, aiming to ascertain the relative usefulness of each one. One ensemble uses sensible choices of physical parameterizations (precipitation microphysics, land surface physics, and cumulus physics) while the other follows a perturbed initial conditions approach. The results show that, depending on the parameterizations, large differences can be expected in terms of storm location, spatial structure of the precipitation field, and rain intensity. It is also found that the spread of the perturbed initial conditions ensemble is smaller than the dispersion due to physical parameterizations. This confirms that in severe weather situations operational forecasts should address moist physics deficiencies to realize the full benefits of the ensemble approach, in addition to optimizing initial conditions. The results also provide insights into differences in simulations arising from ensembles of weather models using several combinations of different physical parameterizations.

severe weather↗

NASA NTRS2015

Adaptive Shape Control for Aerodynamic Design

We present an approach to aerodynamic optimization in which the shape control is adaptively parameterized. Starting from a coarse set of design variables, a sequence of higher-dimensional nested search spaces is automatically generated. Refinement can be either uniform or adaptive, in which case only the most important shape control is added. The relative importance of candidate design variables is determined by comparing objective and constraint gradients, computed at low cost via adjoint solutions. A search procedure for finding an effective ensemble of shape parameters is also given. We first demonstrate this system on a multipoint drag miminization problem in 2D with many constraints, showing that an adaptive parameterization approach consistently achieves smoother, more robust, and faster design improvement than fixed parameterizations. We also establish a 3D shape- matching benchmark, where we demonstrate that our approach automatically discovers the necessary parameters to match a target shape. By largely automating shape parameterization, this work also aims to remove a time-consuming aspect of shape optimization.

Aerodynamic Design↗

NASA NTRS2019

Diagnosing Bias in Modeled Soil Moisture/runoff Coefficient Correlation Using the SMAP Level 4 Soil Moisture Product

The physical parameterization of key processes in land surface models (LSMs) remains uncertain, and new techniques are required to evaluate LSM accuracy over coarse spatial scales. Given the role of soil moisture in the partitioning of surface water fluxes (between infiltration, runoff and evapotranspiration), surface soil moisture (SSM) estimates represent an important observational benchmark for such evaluations. Here, we apply SSM estimates from the NASA Soil Moisture Active Passive Level 4 product (SMAP_L4) to diagnose bias in the coupling between SSM and surface runoff for multiple Noah-Multiple Physics (Noah-MP) LSM parameterization cases. Results demonstrate that Noah-MP surface runoff parameterizations often underestimate the coupling strength between pre-storm SSM and the event-scale runoff coefficient (RC; defined as the ratio between event-scale streamflow and precipitation volumes). This bias squanders RC information contained in pre-storm SSM and reduces RC estimation skill. Such bias can be quantified against an observational benchmark calculated using streamflow observations and SMAP_L4 SSM and applied to explain a substantial fraction of the observed basin-to-basin (and case-to-case) variability in the skill of event-scale Noah-MP RC estimates. Based on this concept, a novel case selection strategy for ungauged basins is introduced and demonstrated to successfully identify poorly performing Noah-MP parameterization cases.

SSM↗

NASA NTRSDate not supplied

Impacts of aerosol size-dependent below-cloud scavenging on tropospheric aerosol in the NASA GEOS model

Wet deposition is the major sink for a suite of tropospheric aerosols, and thus largely affects the global distribution and lifetime of aerosols as well as the formation of clouds in the atmosphere. In this study, we implement the aerosol size-dependent below-cloud scavenging (BCS; washout) parameterization of Croft et al. (Atmos. Chem. Phys., 2009; hereafter referred to as C09) in the GOCART bulk aerosol scheme coupled with the NASA Goddard Earth Observing System (GEOS) model, and evaluate the simulated tropospheric aerosols against surface, airborne, and satellite observations. The current below-cloud scavenging in GOCART employs a first-order removal function with a constant scavenging coefficient that is independent of aerosol sizes and precipitation fluxes. The C09 BCS parameterization instead calculates aerosol below-cloud scavenging coefficients based on a size-dependent collision efficiency between aerosol particles and raindrops (or snow crystals). A look-up table of compiled collision efficiencies provides scavenging coefficients for sixty aerosol sizes and nine rain flux rates. We determine instantaneous coefficients according to the C09 BCS look-up table, precipitation fluxes, and a log-normal size distribution of aerosols. The implemented BCS parameterization tends to result in a large diversity of scavenging coefficient values with higher magnitudes, leading to more efficient scavenging. The model-simulated global mean tropospheric Pb-210 aerosol lifetime decreases from 7.4 days to 5.8 days. The separate consideration of rain and snow conditions in the scavenging coefficient lookup table leads to a bi-mode distribution of scavenging coefficients. These changes also cause increased mass fractions of smaller aerosols compared to the current simulation. We examine the impacts of the C09 BCS parameterization on the simulated aerosol vertical profiles, tropospheric lifetimes, and deposition fluxes through comparisons with NASA aircraft measurements (ATom), surface observations of aerosol concentrations (IMPROVE) and wet deposition fluxes (NADP and APQMP), and MODIS aerosol optical depth (AOD) retrievals.

Aerosols↗

NASA NTRS2020

An Examination of the Impact of Grid Spacing on WRF Simulations of Wintertime Precipitation in the Mid-Atlantic United States

A large set of deterministic and ensemble forecasts was produced to identify the optimal spacing for forecasting U.S. East Coast snowstorms. WRF forecasts were produced on cloud-allowing (~1-km grid spacing) and convection-allowing (3–4 km) grids, and compared against forecasts with parameterized convection (>~10 km). Performance diagrams were used to evaluate 19 deterministic forecasts from the winter of 2013–14. Ensemble forecasts of five disruptive snowstorms spanning the years 2015–18 were evaluated using various methods to evaluate probabilistic forecasts. While deterministic forecasts using cloud-allowing grids were not better than convection-allowing forecasts, both had lower bias and higher success ratios than forecasts with parameterized convection. All forecasts were underdispersive. Nevertheless, forecasts on the higher-resolution grids were more reliable than those with parameterized convection. Forecasts on the cloud-allowing grid were best able to discriminate areas that received heavy snow and those that did not, while the forecasts with parameterized convection were least able to do so. It is recommended to use convection-resolving and (if computationally possible) to use cloud-allowing forecast grids when predicting East Coast winter storms.

Snowstorms↗

NASA NTRS1993

An Algorithm for Forecasting Mountain Wave Related Turbulence in the Stratosphere

A global mountain wave parameterization for prediction of wave related displacements and turbulence is described. The parameterization is used with input from NMC analyses of wind and temperature to examine small-scale disturbances encountered by the NASA high-altitude ER-2 during the Second Airborne Arctic Stratosphere Experiment (AASE-II). The magnitude and location of observed large wave events are well reproduced. A strong correlation is suggested between patches of moderate turbulence encountered by the ER-2 and locations where breaking mountain waves are predicted by the parameterization. These facts suggest that useful forecasts of global mountain wave activity, including wave related CAT, can be made quickly and inexpensively using our mountain wave parameterization with input from current numerical forecast models.

Chan, Roland↗

NASA NTRS2022

The Lower Thermospheric Winter-To-Summer Meridional Circulation: 1: Driving Mechanism

In this study, the mechanism driving the narrow lower-thermospheric winter-to-summer meridional circulation is thoroughly investigated for the first time using the Specified Dynamics configuration runs of the Whole Atmosphere Community Climate Model eXtended (SD-WACCMX) simulations and the TIMED Doppler Interferometer (TIDI) observations. The mean meridional circulation in the SD-WACCMX is qualitatively consistent with the TIDI measurements, though the magnitude in the SD-WACCMX is about 50% weaker. The lower-thermospheric winter-to-summer circulation is mainly driven by the resolved wave forcing, including the tides and internally generated inertia gravity waves (GWs). The momentum forcing from the parameterized sub-grid scale GWs is not as significant as the resolved wave forcing in driving the lower-thermospheric meridional circulation. The GW parameterization scheme in the SD-WACCMX only includes GWs with phase velocities in the range of ±45 m/s, which might result in most of the parameterized sub-grid GWs dissipating and breaking in the mesosphere and hardly impacting the lower thermosphere. Only including slow GWs in the SD-WACCMX parameterization could potentially lead to the underestimation of the meridional wind in the model. Analysis also indicates the lower-thermospheric meridional circulation is stronger in the summer hemisphere, which is attributed to the hemispheric asymmetry in the resolved wave momentum forcing. This study underlines the importance of the whole atmosphere coupling through wave propagation and dissipation. This understanding can guide the model development with an accurate representation of underlying physical processes in the mesosphere and lower thermosphere which drives the lower-thermospheric circulation as well as the overall dynamics of this region.

Jack C. Wang↗

NASA NTRSDate not supplied

Dynamic Adjustment of Model Parameters Using Assimilated Boundary Layer Height

The NASA Global Modeling and Assimilation Office is exploring the assimilation of boundary layer height (PBLH) derived from a variety of observations, including radiosondes, GNSS-RO, space- and ground-based lidar, and radar wind profilers. Previous work using the Goddard Earth Observing System (GEOS) has shown that within the lower troposphere, the representation of thermodynamic structure is strongly affected by model parameterizations. In assimilation experiments, parameterizations can compensate for analysis thermodynamic increments and reduce the observation information carried forward in time. To mitigate this effect, the present study explores a dynamic parameter adjustment based on the PBLH analysis field. The PBLH analysis increment is used to adjust model parameters that influence the depth of parameterized boundary layer mixing in both stable and convective regimes. Replay (nudging) experiments and cycled data assimilation experiments are used to evaluate the approach. The parameter adjustment is shown to reduce model compensation of the analysis thermodynamic tendencies and bring model parameterized mixing depths into closer agreement with observed PBLH.

Nathan Arnold↗

NASA NTRS2015

Improving High-Resolution Weather Forecasts using the Weather Research and Forecasting (WRF) Model with an Updated Kain-Fritsch Scheme

Efforts to improve the prediction accuracy of high resolution (1-10 km) surface precipitation distribution and variability are of vital importance to local aspects of air pollution, wet deposition, and regional climate. However, precipitation biases and errors can occur at these spatial scales due to uncertainties in initial meteorological conditions and/or grid-scale cloud microphysics schemes. In particular, it is still unclear to what extent a subgrid-scale convection scheme could be modified to bring in scale-awareness for improving high-resolution short-term precipitation forecasts in the WRF model. To address these issues, we introduced scale-aware parameterized cloud dynamics for high-resolution forecasts by making several changes to the Kain-Fritsch (KF) convective parameterization scheme in the WRF model. These changes include subgrid-scale cloud-radiation interactions, a dynamic adjustment timescale, impacts of cloud updraft mass fluxes on grid-scale vertical velocity, and lifting condensation level-based entrainment methodology that includes scale dependency. A series of 48-hour retrospective forecasts using a combination of three treatments of convection (KF, updated KF, and the use of no cumulus parameterization), two cloud microphysics schemes and two types of initial condition datasets were performed over the U.S. southern Great Plains on 9- and 3-km grid spacings during the summers of 2002 and 2010. Results indicate that (1) the source of initial conditions play a key role in high-resolution precipitation forecasting, and (2) our updated KF scheme greatly alleviates the excessive precipitation at 9-km grid spacing and improves results at 3-km grid spacing as well. Overall, we found that the updated KF scheme incorporated into a high-resolution model does provide better forecasts for precipitation location and intensity.

convective parameterization↗

NASA NTRS2015

Using Satellite Observations to Infer the Relationship Between Cold Pools and Subsequent Convection Development

Cold pools are increasingly being recognized as important players in the evolution of both shallow and deep convection; hence, the incorporation of cold pool processes into a number of recently developed convective parameterizations. Unfortunately, observations serving to inform cold pool parameterization development are limited to select field programs and limited radar domains. However, a number of recent studies have noted that cold pools are often associated with arcs-lines of shallow clouds traversing 10 100 km in visible satellite imagery. Boundary layer thermodynamic perturbations are plausible at such scales, coincident with such mesoscale features. Atmospheric signatures of features at these spatial scales are potentially observable from satellites. In this presentation, we discuss recent work that uses multi-sensor, high-resolution satellite products for observing mesoscale wind vector fluctuations and boundary layer temperature depressions attributed to cold pools produced by antecedent convection. The relationship to subsequent convection as well as convective system longevity is discussed. As improvements in satellite technology occur and efforts to reduce noise in high-resolution orbital products progress, satellite pixel level (10 km) thermodynamic and dynamic (e.g. mesoscale convergence) parameters can increasingly serve as useful benchmarks for constraining convective parameterization development, including for regimes where organized convection contributes substantially to the cloud and rainfall climatology.

thermodynamics↗

NASA NTRS2014

Data Automata in Scala

The field of runtime verification has during the last decade seen a multitude of systems for monitoring event sequences (traces) emitted by a running system. The objective is to ensure correctness of a system by checking its execution traces against formal specifications representing requirements. A special challenge is data parameterized events, where monitors have to keep track of the combination of control states as well as data constraints, relating events and the data they carry across time points. This poses a challenge wrt. efficiency of monitors, as well as expressiveness of logics. Data automata is a form of automata where states are parameterized with data, supporting monitoring of data parameterized events. We describe the full details of a very simple API in the Scala programming language, an internal DSL (Domain-Specific Language), implementing data automata. The small implementation suggests a design pattern. Data automata allow transition conditions to refer to other states than the source state, and allow target states of transitions to be inlined, offering a temporal logic flavored notation. An embedding of a logic in a high-level language like Scala in addition allows monitors to be programmed using all of Scala's language constructs, offering the full flexibility of a programming language. The framework is demonstrated on an XML processing scenario previously addressed in related work.

runtime verification↗

NASA NTRS2019

The Interaction Between Boundary Layer and Convection Schemes in a WRF Simulation of Post‐Cold‐Frontal Clouds over the ARM East North Atlantic Site

The correct representation of lowlevel midlatitude clouds found in the wake of cold fronts strongly relies on the representation of planetary boundary layer (PBL) and convection processes, which are typically parameterized separately in numerical models. Using the Weather Research and Forecasting Model (WRF), this study investigates how distinct pairs of PBL and convection parameterization schemes represent cloud fraction in the postcoldfrontal region. The simulations focus on the region of the DOEARM Eastern North Atlantic observation site in the Azores Islands in the wake of a cold front that passed on December 25, 2015. Different PBL and convection schemes are combined to create 12 distinct configurations. The main differences between the selected physical parameterizations are the strength of vertical mixing and the entrainment. The simulations produce a wide range of cloud fractions, where some configurations significantly underestimate while others clearly overestimate satellite and surfacebased estimates of cloud fraction. A skill score is used to quantitatively assess the performance of each configuration with respect to groundbased radar data. The key processes that are found to significantly impact the cloud fraction distribution are the strength of the PBL decoupling, the vertical wind shear, entrainment and detrainment rates in shallow convection and the occurrence of drizzle. This indicates that to successfully simulate postcoldfrontal clouds, modeled physics must balance strong internal vertical mixing and weak exchange with the free troposphere. For this case study, cloud fraction was more sensitive to the choice of convection scheme than PBL scheme.

moist convection interaction between parameterizat↗

NASA NTRS2024

Exploring the ENSO modulation of the QBO periods with GISS E2.2 models

Observational studies have shown that the El Niño–Southern Oscillation (ENSO) exerts an influence on the Quasi-Biennial Oscillation (QBO). The downward propagation of the QBO tends to speed up and slow down during El Niño and La Niña, respectively. Recent results from general circulation models have indicated that the ENSO modulation of the QBO requires a relatively high horizontal resolution, and that it does not show up in the climate models with parameterized but temporally constant gravity wave sources. Here, we demonstrate that the NASA Goddard Institute for Space Studies (GISS) E2.2 models can capture the observed ENSO modulation of the QBO period with a horizontal resolution of 2 ∘ latitude by 2.5 ∘ longitude but with its gravity wave sources being parameterized interactively. This is because El Niño events lead to more vigorous gravity wave sources generating more absolute momentum fluxes over the equatorial belt, as well as less filtering of these waves into the tropical lower stratosphere through a weakening of the Walker circulation. Various components of the ENSO system, such as the sea surface temperatures, the convective activities, and the Walker circulation, are intimately involved in the generation and propagation of parameterized gravity waves, through which ENSO modulates the QBO period in GISS E2.2 models.

El Niño–Southern Oscillation↗

NASA NTRS1974

Strategies for estimating the marine geoid from altimeter data

In processing altimeter data from a spacecraft borne altimeter to estimate the fine structure of the marine geoid, a problem is encountered. In order to describe the geoid fine structure, a large number of parameters must be employed and it is not possible to simultaneously estimate all of them. Unless the parameterization exhibits good orthogonality in the data, serious aliasing results. From simulation studies it has been found that amongst several competing parameterizations, the mean free air gravity anomaly model (i.e., Stokes' formula) exhibited promising geoid recovery characteristics. Using covariance analysis techniques, this report provides quantitative measures of the orthogonality properties associated with the above mentioned parameterization. It has been determined that a 5 deg x 5 deg area mean free air gravity anomaly can be estimated with an uncertainty of 1 mgal (40 cm undulation) provided that all free air gravity anomalies within a spherical radius of 10 arc degrees are simultaneously estimated.

Argentiero, P.↗

NASA NTRS1977

A dual-loop model of the human controller in single-axis tracking tasks

A dual loop model of the human controller in single axis compensatory tracking tasks is introduced. This model possesses an inner-loop closure which involves feeding back that portion of the controlled element output rate which is due to control activity. The sensory inputs to the human controller are assumed to be system error and control force. The former is assumed to be sensed via visual, aural, or tactile displays while the latter is assumed to be sensed in kinesthetic fashion. A nonlinear form of the model is briefly discussed. This model is then linearized and parameterized. A set of general adaptive characteristics for the parameterized model is hypothesized. These characteristics describe the manner in which the parameters in the linearized model will vary with such things as display quality. It is demonstrated that the parameterized model can produce controller describing functions which closely approximate those measured in laboratory tracking tasks for a wide variety of controlled elements.

Hess, R. A.↗