Nature Run Research Articles

Sense size‐dependent dust loading and emission from space using reflected solar and infrared spectral measurements: An observation system simulation experiment

AbstractThe Climate Absolute Radiance and Refractivity Observatory (CLARREO) satellite mission observes hyperspectral Earth reflected solar (RS) and emitted infrared radiance (IR). Such measurements span an additional dimension on spectrally dependent scattering and absorption of dust, the critical signals for particle size. Through a suite of observation system simulation experiments (OSSEs), this study assesses the capability of CLARREO's measurements for recovering size‐dependent dust emissions in GEOS‐Chem chemistry transport model (CTM). To this end, another CTM (Flow‐following finite‐volume Icosahedral Model‐Chem, or FIM‐Chem) is used for the nature run to simulate CLARREO spectral radiances. The spectral signals are then used for analyzing the sensitivities and error characteristics of dust optical depth (DOD) under three observations scenarios (IR only, RS only, and combined IR and RS) using an optimal estimation technique. Next, these synthetic data are assimilated into GEOS‐Chem adjoint model to constrain dust emissions of four particle sizes with radii from 0.1 μm to 6.0 μm. The OSSEs results indicate (1) the IR spectra are most sensitive to dust of the third size bin (1.8–3.0 μm) and least sensitive to the smallest bin (0.1–1.0 μm); (2) the RS spectra are most sensitive to dust of the smallest size bin and the sensitivity decreases as dust size increases; (3) combining IR and RS spectra can fully characterize DOD across all sizes, providing the best constraints for size‐resolved dust emissions; and (4) CLARREO data fail to constrain the spatial distribution of dust sources due to its narrow swath and joint observations from CLARREO‐calibrated sensors with wide swath are desirable.

An evaluation of gravity waves and gravity wave sources in the Southern Hemisphere in a 7 km global climate simulation.

In this study, gravity waves (GWs) in the high-resolution GEOS-5 Nature Run are first evaluated with respect to satellite and other model results. Southern Hemisphere winter sources of non-orographic GWs in the model are then investigated by linking measures of tropospheric non-orographic gravity wave generation tied to precipitation and frontogenesis with absolute gravity wave momentum flux in the lower stratosphere. Finally, non-orographic GW momentum flux is compared to orographic gravity wave momentum flux and compared to previous estimates. The results show that the global patterns in GW amplitude, horizontal wavelength, and propagation direction are realistic compared to observations. However, as in other global models, the amplitudes are weaker and horizontal wavelengths longer than observed. The global patterns in absolute GW momentum flux also agree well with previous model and observational estimates. The evaluation of model non-orographic GW sources in the Southern Hemisphere winter shows that strong intermittent precipitation (greater than 10 mm h-1) is associated with GW momentum flux over the South Pacific, whereas frontogenesis and less intermittent, lower precipitation rates (less than 10 mm h-1) are associated with GW momentum flux near 60°S. In the model, orographic GWs contribute almost exclusively to a peak in zonal mean momentum flux between 70 and 75°S, while non-orographic waves dominate at 60°S, and non-orographic GWs contribute a third to a peak in zonal mean momentum flux between 25 and 30°S.

North Atlantic Ocean OSSE system: Evaluation of operational ocean observing system components and supplemental seasonal observations for potentially improving tropical cyclone prediction in coupled systems

ABSTRACTObserving System Simulated Experiments (OSSEs) performed during the 2014 North Atlantic hurricane season quantify ocean observing system impacts with respect to improving ocean model initialisation in coupled tropical cyclone (TC) prediction systems. The suitability of the OSSE system forecast model (FM) with respect to the previously validated Nature Run is demonstrated first. Analyses are then performed to determine the calibration required to obtain credible OSSE impact assessments. Impacts on errors and biases in fields important to TC prediction are first quantified for three major components of the existing operational ocean observing system. Satellite altimetry provides the greatest positive impact, followed by Argo floats and sea surface temperature measurements from both satellite and in-situ systems. The OSSE system is then used to investigate observing system enhancements, specifically regional underwater glider deployments during the 2014 hurricane season. These deployments resulted in modest positive impacts on ocean analyses that were limited by (1) errors in the horizontal structure of the increment field imposed by individual gliders and (2) memory loss in the spreading of these corrections by nonlinear model dynamics. The high-resolution, three-dimensional representation of the truth available in OSSE systems allows these issues to be studied without high-density ocean observations.

On the mesoscale monitoring capability of Argo floats in the Mediterranean Sea

Abstract. In this work a simplified observing system simulation experiment (OSSE) approach is used to investigate which Argo design sampling in the Mediterranean Sea would be necessary to properly capture the mesoscale dynamics in this basin. The monitoring of the mesoscale features is not an initial objective of the Argo network. However, it is an interesting question from the perspective of future network extensions in order to improve the ocean state estimates. The true field used to conduct the OSSEs is provided by a specific altimetry-gridded merged product for the Mediterranean Sea. Synthetic observations were obtained by sub-sampling this Nature Run according to different configurations of the ARGO network. The observation errors required to perform the OSSEs were obtained through the comparison of sea level anomalies (SLAs) from altimetry and dynamic height anomalies (DHAs) computed from the real in situ Argo network. This analysis also contributes to validate satellite SLAs with an increased confidence. The simulation experiments show that a configuration similar to the current Argo array in the Mediterranean (with a spatial resolution of 2° × 2°) is only able to recover the large-scale signals of the basin. Increasing the spatial resolution to nearly 75 km × 75 km, allows the capture of most of the mesoscale signal in the basin and to retrieve the SLA field with a RMSE of 3 cm for spatial scales larger than 150 km, similar to those presently captured by the altimetry. This would represent a theoretical reduction of 40 % of the actual RMSE. Such a high-resolution Argo array composed of around 450 floats, cycling every 10 days, is expected to increase the actual network cost by approximately a factor of 6.

A Severe Weather Quick Observing System Simulation Experiment (QuickOSSE) of Global Navigation Satellite System (GNSS) Radio Occultation (RO) Superconstellations

AbstractGlobal Navigation Satellite System (GNSS) radio occultations (RO) over the last 10 years have proved to be a valuable and essentially unbiased data source for operational global numerical weather prediction. However, the existing sampling coverage is too sparse in both space and time to support forecasting of severe mesoscale weather. In this study, the case study or quick observing system simulation experiment (QuickOSSE) framework is used to quantify the impact of vastly increased numbers of GNSS RO profiles on mesoscale weather analysis and forecasting. The current study focuses on a severe convective weather event that produced both a tornado and flash flooding in Oklahoma on 31 May 2013. The WRF Model is used to compute a realistic and faithful depiction of reality. This 2-km “nature run” (NR) serves as the “truth” in this study. The NR is sampled by two proposed constellations of GNSS RO receivers that would produce 250 thousand and 2.5 million profiles per day globally. These data are then assimilated using WRF and a 24-member, 18-km-resolution, physics-based ensemble Kalman filter. The data assimilation is cycled hourly and makes use of a nonlocal, excess phase observation operator for RO data. The assimilation of greatly increased numbers of RO profiles produces improved analyses, particularly of the lower-tropospheric moisture fields. The forecast results suggest positive impacts on convective initiation. Additional experiments should be conducted for different weather scenarios and with improved OSSE systems.

Impact of spaceborne carbon monoxide observations from the S-5P platform on tropospheric composition analyses and forecasts

Abstract. We use the technique of Observing System Simulation Experiments (OSSEs) to quantify the impact of spaceborne carbon monoxide (CO) total column observations from the Sentinel-5 Precursor (S-5P) platform on tropospheric analyses and forecasts. We focus on Europe for the period of northern summer 2003, when there was a severe heat wave episode associated with extremely hot and dry weather conditions. We describe different elements of the OSSE: (i) the nature run (NR), i.e., the truth; (ii) the CO synthetic observations; (iii) the assimilation run (AR), where we assimilate the observations of interest; (iv) the control run (CR), in this study a free model run without assimilation; and (v) efforts to establish the fidelity of the OSSE results. Comparison of the results from AR and the CR, against the NR, shows that CO total column observations from S-5P provide a significant benefit (at the 99 % confidence level) at the surface, with the largest benefit occurring over land in regions far away from emission sources. Furthermore, the S-5P CO total column observations are able to capture phenomena such as the forest fires that occurred in Portugal during northern summer 2003. These results provide evidence of the benefit of S-5P observations for monitoring processes contributing to atmospheric pollution.

Tropical Cyclones in the 7km NASA Global Nature Run for use in Observing System Simulation Experiments.

The National Aeronautics and Space Administration (NASA) Nature Run (NR), released for use in Observing System Simulation Experiments (OSSEs), is a 2-year long global non-hydrostatic free-running simulation at a horizontal resolution of 7 km, forced by observed sea-surface temperatures (SSTs) and sea ice, and inclusive of interactive aerosols and trace gases. This article evaluates the NR with respect to tropical cyclone (TC) activity. It is emphasized that to serve as a NR, a long-term simulation must be able to produce realistic TCs, which arise out of realistic large-scale forcings. The presence in the NR of the realistic, relevant dynamical features over the African Monsoon region and the tropical Atlantic is confirmed, along with realistic African Easterly Wave activity. The NR Atlantic TC seasons, produced with 2005 and 2006 SSTs, show interannual variability consistent with observations, with much stronger activity in 2005. An investigation of TC activity over all the other basins (eastern and western North Pacific, North and South Indian Ocean, and Australian region), together with relevant elements of the atmospheric circulation, such as, for example, the Somali Jet and westerly bursts, reveals that the model captures the fundamental aspects of TC seasons in every basin, producing realistic number of TCs with realistic tracks, life spans and structures. This confirms that the NASA NR is a very suitable tool for OSSEs targeting TCs and represents an improvement with respect to previous long simulations that have served the global atmospheric OSSE community.

Impact of Assimilating CYGNSS Data on Tropical Cyclone Analyses and Forecasts in a Regional OSSE Framework

AbstractThe impact of assimilating ocean surface wind observations from the Cyclone Global Navigation Satellite System (CYGNSS) is examined in a high-resolution Observing System Simulation Experiment (OSSE) framework for tropical cyclones (TCs). CYGNSS is a planned National Aeronautics and Space Administration constellation of microsatellites that utilizes existing GNSS satellites to retrieve surface wind speed. In the OSSE, CYGNSS wind speed data are simulated using output from a “nature run” as truth. In a case study using the regional Hurricane Weather Research and Forecasting modeling system and the Gridpoint Statistical Interpolation data assimilation scheme, analyses of TC position, structure, and intensity, together with large-scale variables, are improved due to the assimilation of the additional surface wind data. These results indicate the potential importance of CYGNSS ocean surface wind speed data and furthermore that the assimilation of directional information would add further value to TC analyses and forecasts.

Assimilating synthetic hyperspectral sounder temperature and humidity retrievals to improve severe weather forecasts

Assimilation of hyperspectral sounder data into numerical weather prediction (NWP) models has proven vital to generating accurate model analyses of tropospheric temperature and humidity where few conventional observations exist. Applications to storm-scale models are limited since the low temporal resolution provided by polar orbiting sensors cannot adequately sample rapidly changing environments associated with high impact weather events. To address this limitation, hyperspectral sounders have been proposed for geostationary orbiting satellites, but these have yet to be built and launched in part due to much higher engineering costs and a lack of a definite requirement for the data.This study uses an Observation System Simulation Experiment (OSSE) approach to simulate temperature and humidity profiles from a hypothetical geostationary-based sounder from a nature run of a high impact weather event on 20 May 2013. The simulated observations are then assimilated using an ensemble adjustment Kalman filter approach, testing both hourly and 15minute cycling to determine their relative effectiveness at improving the near storm environment. Results indicate that assimilating both temperature and humidity profiles reduced mid-tropospheric both mean and standard deviation of analysis and forecast errors compared to assimilating conventional observations alone. The 15minute cycling generally produced the lowest errors while also generating the best 2–4hour updraft helicity forecasts of ongoing convection. This study indicates the potential for significant improvement in short-term forecasting of severe storms from the assimilation of hyperspectral geostationary satellite data. However, more studies are required using improved OSSE designs encompassing multiple storm environments and additional observation types such as radar reflectivity to fully define the effectiveness of assimilating geostationary hyperspectral observations for high impact weather forecasting applications.

Examining the Potential Impact of SWOT Observations in an Ocean Analysis–Forecasting System

AbstractNASA’s Surface Water and Ocean Topography (SWOT) satellite, scheduled for launch in 2020, will provide observations of sea surface height anomaly (SSHA) at a significantly higher spatial resolution than current satellite altimeters. This new observation type is expected to improve the ocean model mesoscale circulation. The potential improvement that SWOT will provide is investigated in this work by way of twin-data assimilation experiments using the Navy Coastal Ocean Model four-dimensional variational data assimilation (NCOM-4DVAR) system in its weak constraint formulation. Simulated SWOT observations are sampled from an ocean model run (referred to as the “nature” run) using an observation-simulator program provided by the SWOT science team. The SWOT simulator provides realistic spatial coverage, resolution, and noise characteristics based on the expected performance of the actual satellite. Twin-data assimilation experiments are run for a two-month period during which simulated observations are assimilated into a separate model (known as the background model) in a series of 96-h windows. The final condition of each analysis window is used to initialize a new 96-h forecast, and each forecast is compared to the nature run to determine the impact of the assimilated data. It is demonstrated here that the simulated SWOT observations help to constrain the model mesoscale to be more consistent with the nature run than the assimilation of traditional altimeter observations alone. The findings of this study suggest that data from SWOT may have a substantial impact on improving the ocean model forecast of mesoscale features and surface ocean velocity.

North Atlantic Ocean OSSE system development: Nature Run evaluation and application to hurricane interaction with the Gulf Stream

A high resolution, free-running model has been developed for the hurricane region of the North Atlantic Ocean. The model is evaluated with a variety of observations to ensure that it adequately represents both the ocean climatology and variability over this region, with a focus on processes relevant to hurricane-ocean interactions. As such, it can be used as the “Nature Run” (NR) model within the framework of Observing System Simulation Experiments (OSSEs), designed specifically to improve the ocean component of coupled ocean-atmosphere hurricane forecast models. The OSSE methodology provides quantitative assessment of the impact of specific observations on the skill of forecast models and enables the comprehensive design of future observational platforms and the optimization of existing ones. Ocean OSSEs require a state-of-the-art, high-resolution free-running model simulation that represents the true ocean (the NR). This study concentrates on the development and data based evaluation of the NR model component, which leads to a reliable model simulation that has a dual purpose: (a) to provide the basis for future hurricane related OSSEs; (b) to explore process oriented studies of hurricane-ocean interactions. A specific example is presented, where the impact of Hurricane Bill (2009) on the eastward extension and transport of the Gulf Stream is analyzed. The hurricane induced cold wake is shown in both NR simulation and observations. Interaction of storm-forced currents with the Gulf Stream produced a temporary large reduction in eastward transport downstream from Cape Hatteras and had a marked influence on frontal displacement in the upper ocean. The kinetic energy due to ageostrophic currents showed a significant increase as the storm passed, and then decreased to pre-storm levels within 8 days after the hurricane advanced further north. This is a unique result of direct hurricane impact on a western boundary current, with possible implications on the ocean feedback on hurricane evolution.

Tropical Waves and the Quasi-Biennial Oscillation in a 7-km Global Climate Simulation

Abstract This study investigates tropical waves and their role in driving a quasi-biennial oscillation (QBO)-like signal in stratospheric winds in a global 7-km-horizontal-resolution atmospheric general circulation model. The Nature Run (NR) is a 2-yr global mesoscale simulation of the Goddard Earth Observing System Model, version 5 (GEOS-5). In the tropics, there is evidence that the NR supports a broad range of convectively generated waves. The NR precipitation spectrum resembles the observed spectrum in many aspects, including the preference for westward-propagating waves. However, even with very high horizontal resolution and a healthy population of resolved waves, the zonal force provided by the resolved waves is still too low in the QBO region and parameterized gravity wave drag is the main driver of the NR QBO-like oscillation (NR-QBO). The authors suggest that causes include coarse vertical resolution and excessive dissipation. Nevertheless, the very-high-resolution NR provides an opportunity to analyze the resolved wave forcing of the NR-QBO. In agreement with previous studies, large-scale Kelvin and small-scale waves contribute to the NR-QBO driving in eastward shear zones and small-scale waves dominate the NR-QBO driving in westward shear zones. Waves with zonal wavelength < 1000 km account for up to half of the small-scale (<3300 km) resolved wave forcing in eastward shear zones and up to 70% of the small-scale resolved wave forcing in westward shear zones of the NR-QBO.

Community Global Observing System Simulation Experiment (OSSE) Package (CGOP): Description and Usage

AbstractA modular extensible framework for conducting observing system simulation experiments (OSSEs) has been developed with the goals of 1) supporting decision-makers with quantitative assessments of proposed observing systems investments, 2) supporting readiness for new sensors, 3) enhancing collaboration across the community by making the most up-to-date OSSE components accessible, and 4) advancing the theory and practical application of OSSEs. This first implementation, the Community Global OSSE Package (CGOP), is for short- to medium-range global numerical weather prediction applications. The CGOP is based on a new mesoscale global nature run produced by NASA using the 7-km cubed sphere version of the Goddard Earth Observing System, version 5 (GEOS-5), atmospheric general circulation model and the January 2015 operational version of the NOAA global data assimilation (DA) system. CGOP includes procedures to simulate the full suite of observing systems used operationally in the global DA system, including conventional in situ, satellite-based radiance, and radio occultation observations. The methodology of adding a new proposed observation type is documented and illustrated with examples of current interest. The CGOP is designed to evolve, both to improve its realism and to keep pace with the advance of operational systems.

Study of conservation laws with the Local Ensemble Transform Kalman Filter

Numerical discretization schemes have a long history of incorporating the most important conservation properties of the continuous system in order to improve the prediction of the nonlinear flow. The question arises whether data assimilation algorithms should follow a similar approach. To address this issue, we explore the conservation properties during data assimilation using perfect model experiments with a 2D shallow‐water model preserving important properties of the true nonlinear flow. The data assimilation scheme used here is the Local Ensemble Transform Kalman Filter with varying observed variables, inflation, localization radius and thinning interval. It is found that, during the assimilation, the total energy of the analysis ensemble mean converges with time towards the nature run value. However, enstrophy, divergence and the energy spectra are strongly affected by the data assimilation settings. Having in mind that the conservation of both the kinetic energy and enstrophy by the momentum advection schemes in the case of non‐divergent flow prevents a systematic and unrealistic energy cascade towards the high wave numbers, we test the effects on the prediction depending on the type of error in the initial condition. During the assimilation, we assess the downward nonlinear energy cascade through a scalar, domain‐averaged noise measure. We show that the accumulated noise during assimilation and the error of analysis are good indicators of the quality of the prediction.

On the feasibility of monitoring carbon monoxide in the lower troposphere from a constellation of northern hemisphere geostationary satellites: Global scale assimilation experiments (Part II)

This paper describes the second phase of an Observing System Simulation Experiment (OSSE) that utilizes the synthetic measurements from a constellation of satellites measuring atmospheric composition from geostationary (GEO) Earth orbit presented in part I of the study. Our OSSE is focused on carbon monoxide observations over North America, East Asia and Europe where most of the anthropogenic sources are located. Here we assess the impact of a potential GEO constellation on constraining northern hemisphere (NH) carbon monoxide (CO) using data assimilation. We show how cloud cover affects the GEO constellation data density with the largest cloud cover (i.e., lowest data density) occurring during Asian summer. We compare the modeled state of the atmosphere (Control Run), before CO data assimilation, with the known “true” state of the atmosphere (Nature Run) and show that our setup provides realistic atmospheric CO fields and emission budgets. Overall, the Control Run underestimates CO concentrations in the northern hemisphere, especially in areas close to CO sources. Assimilation experiments show that constraining CO close to the main anthropogenic sources significantly reduces errors in NH CO compared to the Control Run. We assess the changes in error reduction when only single satellite instruments are available as compared to the full constellation. We find large differences in how measurements for each continental scale observation system affect the hemispherical improvement in long-range transport patterns, especially due to seasonal cloud cover. A GEO constellation will provide the most efficient constraint on NH CO during winter when CO lifetime is longer and increments from data assimilation associated with source regions are advected further around the globe.

Study of the Effect of Temporal Sampling Frequency on DSCOVR Observations Using the GEOS-5 Nature Run Results (Part II): Cloud Coverage

This is the second part of a study on how temporal sampling frequency affects satellite retrievals in support of the Deep Space Climate Observatory (DSCOVR) mission. Continuing from Part 1, which looked at Earth’s radiation budget, this paper presents the effect of sampling frequency on DSCOVR-derived cloud fraction. The output from NASA’s Goddard Earth Observing System version 5 (GEOS-5) Nature Run is used as the “truth”. The effect of temporal resolution on potential DSCOVR observations is assessed by subsampling the full Nature Run data. A set of metrics, including uncertainty and absolute error in the subsampled time series, correlation between the original and the subsamples, and Fourier analysis have been used for this study. Results show that, for a given sampling frequency, the uncertainties in the annual mean cloud fraction of the sunlit half of the Earth are larger over land than over ocean. Analysis of correlation coefficients between the subsamples and the original time series demonstrates that even though sampling at certain longer time intervals may not increase the uncertainty in the mean, the subsampled time series is further and further away from the “truth” as the sampling interval becomes larger and larger. Fourier analysis shows that the simulated DSCOVR cloud fraction has underlying periodical features at certain time intervals, such as 8, 12, and 24 h. If the data is subsampled at these frequencies, the uncertainties in the mean cloud fraction are higher. These results provide helpful insights for the DSCOVR temporal sampling strategy.

Study of the Effect of Temporal Sampling Frequency on DSCOVR Observations Using the GEOS-5 Nature Run Results (Part I): Earth’s Radiation Budget

Satellites always sample the Earth-atmosphere system in a finite temporal resolution. This study investigates the effect of sampling frequency on the satellite-derived Earth radiation budget, with the Deep Space Climate Observatory (DSCOVR) as an example. The output from NASA’s Goddard Earth Observing System Version 5 (GEOS-5) Nature Run is used as the truth. The Nature Run is a high spatial and temporal resolution atmospheric simulation spanning a two-year period. The effect of temporal resolution on potential DSCOVR observations is assessed by sampling the full Nature Run data with 1-h to 24-h frequencies. The uncertainty associated with a given sampling frequency is measured by computing means over daily, monthly, seasonal and annual intervals and determining the spread across different possible starting points. The skill with which a particular sampling frequency captures the structure of the full time series is measured using correlations and normalized errors. Results show that higher sampling frequency gives more information and less uncertainty in the derived radiation budget. A sampling frequency coarser than every 4 h results in significant error. Correlations between true and sampled time series also decrease more rapidly for a sampling frequency less than 4 h.

Spectral analysis of forecast error investigated with an observing system simulation experiment

The spectra of analysis and forecast error are examined using the observing system simulation experiment framework developed at the National Aeronautics and Space Administration Global Modeling and Assimilation Office. A global numerical weather prediction model, the Global Earth Observing System version 5 with Gridpoint Statistical Interpolation data assimilation, is cycled for 2 months with once-daily forecasts to 336 hours to generate a Control case. Verification of forecast errors using the nature run (NR) as truth is compared with verification of forecast errors using self-analysis; significant underestimation of forecast errors is seen using self-analysis verification for up to 48 hours. Likewise, self-analysis verification significantly overestimates the error growth rates of the early forecast, as well as mis-characterising the spatial scales at which the strongest growth occurs. The NR-verified error variances exhibit a complicated progression of growth, particularly for low wavenumber errors. In a second experiment, cycling of the model and data assimilation over the same period is repeated, but using synthetic observations with different explicitly added observation errors having the same error variances as the control experiment, thus creating a different realisation of the control. The forecast errors of the two experiments become more correlated during the early forecast period, with correlations increasing for up to 72 hours before beginning to decrease.

Assimilating Cloud Water Path as a Function of Model Cloud Microphysics in an Idealized Simulation

Abstract The sensitivity of assimilating satellite retrievals of cloud water path (CWP) to the microphysics scheme used by a convection-allowing numerical model is explored. All experiments use the Advanced Research core of the Weather Research and Forecasting Model (WRF-ARW), with observations assimilated using the Data Assimilation Research Testbed ensemble adjustment Kalman filter and a 40-member ensemble. Three-dimensional idealized supercell simulations are generated from a deterministic WRF nature run started from a homogeneous set of initial conditions. Four cloud microphysics schemes are tested: Lin–Farley–Orville (LFO), Thompson (THOMP), Morrison double-moment (MOR), and Milbrandt–Yau (MY). For the idealized experiments, assimilating CWP generates a mature supercell after approximately 1 h for all microphysics schemes. Vertical profiles of ensemble covariances show large differences in the relationship between CWP and various hydrometeor mixing ratios. While the differences in overall CWP are small, the experiments generate very different reflectivity analyses of the simulated storm, with MOR and MY underestimating reflectivity by a large margin. Vertical profiles of hydrometeor mixing ratios from each experiment are generally consistent with scheme design, such that the Thompson scheme characterizes the storm top as mostly snow whereas the Milbrandt–Yau scheme characterizes the storm top as mostly ice. The impacts of these differences on 30-min forecasts show that MOR and MY are unable to maintain convection within the model while THOMP and LFO perform somewhat better, though all fail to capture the divergent movement of the storm split in the nature run.

Impact of Different Satellite Wind Lidar Telescope Configurations on NCEP GFS Forecast Skill in Observing System Simulation Experiments

AbstractThe Global Wind Observing Sounder (GWOS) concept, which has been developed as a hypothetical space-based hybrid wind lidar system by NASA in response to the 2007 National Research Council (NRC) decadal survey, is expected to provide global wind profile observations with high vertical resolution, precision, and accuracy when realized. The assimilation of Doppler wind lidar (DWL) observations anticipated from the GWOS is being conducted as a series of observing system simulation experiments (OSSEs) at the Joint Center for Satellite Data Assimilation (JCSDA). A companion paper (Riishøjgaard et al.) describes the simulation of this lidar wind data and evaluates the impact on global numerical weather prediction (NWP) of the baseline GWOS using a four-telescope configuration to provide independent line-of-sight wind speeds, while this paper sets out to assess the NWP impact of GWOS equipped with alternative paired configurations of telescopes. The National Centers for Environmental Prediction (NCEP) Gridpoint Statistical Interpolation (GSI) analysis system and the Global Forecast System (GFS) were used, at a resolution of T382 with 64 layers, as the assimilation system and the forecast model, respectively, in these lidar OSSEs. A set of 45-day assimilation and forecast experiments from 2 July to 15 August 2005 was set up and executed.In this OSSE study, a control simulation utilizing all of the data types assimilated in the operational GSI/GFS system was compared to three OSSE simulations that added lidar wind data from the different configurations of telescopes (one-, two-, and four-look configurations). First, the root-mean-square error (RMSE) of wind analysis is compared against the nature run. A significant reduction of the stratospheric RMSE of wind analyses is found for all latitudes when lidar wind profiles are used in the assimilation system. The forecast impacts of lidar data on the wind and mass forecasts are also presented. In addition, the anomaly correlations (AC) of geopotential height forecasts at 500 hPa were evaluated to compare the control and different GWOS telescope configuration experiments. The results show that the assimilation of lidar data from the GWOS (one, two, or four looks) can improve the NCEP GFS wind and mass field forecasts. The addition of the simulated lidar wind observations leads to a statistically significant increase in AC scores.