Abstract

The simulation of observations—a critical Community Global Observing System Simulation Experiment (OSSE) Package (CGOP) component—is validated first by a comparison of error-free simulated observations for the first 24 h at the start of the nature run (NR) to the real observations for those sensors that operated during that period. Sample results of this validation are presented here for existing low-Earth-orbiting (LEO) infrared (IR) and microwave (MW) brightness temperature (BT) observations, for radio occultation (RO) bending angle observations, and for various types of conventional observations. For sensors not operating at the start of the NR, a qualitative validation is obtained by comparing geographic and statistical characteristics of observations over the initial day for such a sensor and an existing similar sensor. The comparisons agree, with no significant unexplained bias, and to within the uncertainties caused by real observation errors, time and space collocation differences, radiative transfer uncertainties, and differences between the NR and reality. To validate channels of a proposed future MW sensor with no equivalent existing spaceborne sensor channel, multiple linear regression is used to relate these channels to existing similar channels. The validation then compares observations simulated from the NR to observations predicted by the regression relationship applied to actual real observations of the existing channels. Overall, the CGOP simulations of error-free observations from conventional and satellite platforms that make up the global observing system are found to be reasonably accurate and suitable as a starting point for creating realistic simulated observations for OSSEs. These findings complete a critical step in the CGOP validation, thereby reducing the caveats required when interpreting the OSSE results.

Abstract

The ocean surface wind mediates exchanges between the ocean and the atmosphere. These air–sea exchange processes are critical for understanding and predicting atmosphere, ocean, and wave phenomena on many time and space scales. A cross-calibrated multiplatform (CCMP) long-term data record of satellite ocean surface winds is available from 1987 to 2008 with planned extensions through 2012. A variational analysis method (VAM) is used to combine surface wind data derived from conventional and in situ sources and multiple satellites into a consistent nearglobal analysis at 25-km resolution, every 6 h. The input data are cross-calibrated wind speeds derived from the Special Sensor Microwave Imager (SSM/I; F08–F15), the Tropical Rainfall Measuring Mission Microwave Imager (TMI), and the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E), and wind vectors from SeaWinds on the NASA Quick Scatterometer (QuikSCAT) and on the second Japanese Advanced Earth Observing Satellite (ADEOS- 2; i.e., the Midori-2 satellite). These are combined with ECMWF reanalyses and operational analyses by the VAM. VAM analyses and derived data are currently available for interested investigators through the Jet Propulsion Laboratory (JPL) Physical Oceanography Distributed Active Archive Center (PO.DAAC). This paper describes the methodology used to assimilate the input data along with the validation and evaluation of the derived CCMP products.

A supplement to this article is available online:

DOI: 10.1175/2010BAMS2946.2

Abstract

The empirical cumulative density function (ECDF) approach can be used to combine multiple, diverse assessment metrics into summary assessment metrics (SAMs) to analyze the results of impact experiments and preoperational implementation testing with numerical weather prediction (NWP) models. The main advantages of the ECDF approach are that it is amenable to statistical significance testing and produces results that are easy to interpret because the SAMs for various subsets tend to vary smoothly and in a consistent manner. In addition, the ECDF approach can be applied in various contexts thanks to the flexibility allowed in the definition of the reference sample.

The interpretations of the examples presented here of the impact of potential future data gaps are consistent with previously reported conclusions. An interesting finding is that the impact of observations decreases with increasing forecast time. This is interpreted as being caused by the masking effect of NWP model errors increasing to become the dominant source of forecast error.

Abstract

A global observing system simulation experiment (OSSE) was used to assess the potential impact of a proposed Global Navigation Satellite System (GNSS) radio occultation (RO) constellation on tropical cyclone (TC) track, maximum 10-m wind speed (V _max), and integrated kinetic energy (IKE) forecasts. The OSSE system was based on the 7-km NASA nature run and simulated RO refractivity determined by the spatial distribution of observations from the original planned (i.e., including both equatorial and polar orbits) Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (COSMIC-2). Data were assimilated using the NOAA operational weather analysis and forecasting system. Three experiments generated global TC track, V _max, and IKE forecasts over 6 weeks of the North Atlantic hurricane season in the North Atlantic, east Pacific, and west Pacific basins. Confidence in our results was bolstered because track forecast errors were similar to those of official National Hurricane Center forecasts, and V _max errors and IKE errors showed similar results. GNSS-RO assimilation did not significantly impact global track forecasts, but did slightly degrade V _max and IKE forecasts in the first 30–60 h of lead time. Global forecast error statistics show adding or excluding explicit random errors to RO profiles made little difference to forecasts. There was large forecast-to-forecast variability in RO impact. For two cases studied in depth, track and V _max improvements and degradations were traced backward through the previous 24 h of assimilation cycles. The largest V _max degradation was traced to particularly good control analyses rather than poor analyses caused by GNSS-RO.

Abstract

Hurricane Joaquin (2015) was characterized by high track forecast uncertainty when it approached the Bahamas from 29 September 2015 to 1 October 2015, with 5-day track predictions ranging from landfall in the United States to east of Bermuda. The source of large track spread in Joaquin forecasts is investigated using an ensemble prediction system (EPS) based on the Hurricane Weather Research and Forecasting (HWRF) Model. For the first time, a high-resolution analysis of an HWRF-based EPS is performed to isolate the factors that control tropical cyclone (TC) track uncertainty. Differences in the synoptic-scale environment, the TC vortex structure, and the TC location are evaluated to understand the source of track forecast uncertainty associated with Joaquin, especially at later lead times when U.S. landfall was possible. EPS members that correctly propagated Joaquin into the central North Atlantic are compared with members that incorrectly predicted U.S. landfall. Joaquin track forecasts were highly dependent on the evolution of the environment, including weak atmospheric steering flow near the Bahamas and three synoptic-scale systems: a trough over North America, a ridge to the northeast of Joaquin, and an upper-tropospheric trough to the east of Joaquin. Differences in the steering flow were associated with perturbations of the synoptic-scale environment at the model initialization time. Ultimately, members that produced a more progressive midlatitude synoptic-scale pattern had reduced track errors. Joaquin track forecast uncertainty was not sensitive to the TC vortex structure or the initial TC position.

Abstract

This paper provides an account of the performance of an experimental version of the Hurricane Weather Research and Forecasting system (HWRFX) for 87 cases of Atlantic tropical cyclones during the 2005, 2007, and 2009 hurricane seasons. The HWRFX system was used to study the influence of model grid resolution, initial conditions, and physics. For each case, the model was run to produce 126 h of forecast with two versions of horizontal resolution, namely, (i) a parent domain at a resolution of about 27 km with a 9-km moving nest (27:9) and (ii) a parent domain at a resolution of 9 km with a 3-km moving nest (9:3). The former was selected to be consistent with the current operational resolution, while the latter is the first step in testing the impact of finer resolutions for future versions of the operational model. The two configurations were run with initial conditions for tropical cyclones obtained from the operational Geophysical Fluid Dynamics Laboratory (GFDL) and HWRF models. Sensitivity experiments were also conducted with the physical parameterization scheme. The study shows that the 9:3 HWRFX system using the GFDL initial conditions and a system of physics similar to the operational version (HWRF) provides the best results in terms of both track and intensity prediction. Use of the HWRF initial conditions in the HWRFX model provides reasonable skill, particularly when used in cases with initially strong storms (hurricane strength). However, initially weak storms (below hurricane strength) posed special challenges for the models. For the weaker storm cases, none of the predictions from the HWRFX runs or the operational GFDL forecasts provided any consistent improvement when compared to the operational Statistical Hurricane Intensity Prediction Scheme with an inland decay component (DSHIPS).

Abstract

The Hurricane Weather Research and Forecasting Model (HWRF) was operationally implemented with a 27-km outer domain and a 9-km moving nest in 2007 (H007) as a tropical cyclone forecast model for the North Atlantic and eastern Pacific hurricane basins. During the 2012 hurricane season, a modified version of HWRF (H212), which increased horizontal resolution by adding a third (3 km) nest within the 9-km nest, replaced H007. H212 thus became the first operational model running at convection-permitting resolution. In addition, there were modifications to the initialization, model physics, tracking algorithm, etc. This paper compares H212 hindcast forecasts for the 2010–11 Atlantic hurricane seasons with forecasts from H007 and H3GP, a triply nested research version of HWRF. H212 reduced track forecast errors for almost all forecast times versus H007 and H3GP. H3GP was superior for intensity forecasts, although H212 showed some improvement over H007. Stratifying the cases by initial vertical wind shear revealed that the main weakness for H212 intensity forecasts was for cases with initially high shear. In these cases, H212 over- and under-intensified storms that were initially stronger and weaker, respectively. These results suggest the primary deficiency negatively impacting H212 intensity forecasts, especially in cases of rapid intensification, was that physics calls were too infrequent for the 3-km inner mesh. Correcting this deficiency along with additional modifications in the 2013 operational version yielded improved track and intensity forecasts. These intensity forecasts were comparable to statistical–dynamical models, showing that dynamical models can contribute to a decrease in operational forecast errors.

The deployment of a space-based Doppler lidar would provide information that is fundamental to advancing the understanding and prediction of weather and climate.

This paper reviews the concepts of wind measurement by Doppler lidar, highlights the results of some observing system simulation experiments with lidar winds, and discusses the important advances in earth system science anticipated with lidar winds.

Observing system simulation experiments, conducted using two different general circulation models, have shown 1) that there is a significant improvement in the forecast accuracy over the Southern Hemisphere and tropical oceans resulting from the assimilation of simulated satellite wind data, and 2) that wind data are significantly more effective than temperature or moisture data in controlling analysis error. Because accurate wind observations are currently almost entirely unavailable for the vast majority of tropical cyclones worldwide, lidar winds have the potential to substantially improve tropical cyclone forecasts. Similarly, to improve water vapor flux divergence calculations, a direct measure of the ageostrophic wind is needed since the present level of uncertainty cannot be reduced with better temperature and moisture soundings alone.

Abstract

The potential impact of Doppler wind lidar (DWL) observations from a proposed optical autocovariance wind lidar (OAWL) instrument is quantified in observing system simulation experiments (OSSEs). The OAWL design would provide profiles of useful wind vectors along a ground track to the left of the International Space Station (ISS), which is in a 51.6° inclination low-Earth orbit (LEO). These observations are simulated realistically, accounting for cloud and aerosol distributions inferred from the OSSE nature runs (NRs), and measurement and sampling error sources. The impact of the simulated observations is determined in both global and regional OSSE frameworks. The global OSSE uses the ECMWF T511 NR and the NCEP operational Global Data Assimilation System at T382 resolution. The regional OSSE uses an embedded hurricane NR and the NCEP operational HWRF data assimilation system with outer and inner domains of 9- and 3-km resolution, respectively.

The global OSSE results show improved analyses and forecasts of tropical winds and extratropical geopotential heights. The tropical wind RMSEs are significantly reduced in the analyses and in short-term forecasts. The tropical wind improvement decays as the forecasts lengthen. The regional OSSEs are limited but show some improvements in hurricane track and intensity forecasts.

A multiscale modeling framework (MMF), which replaces the conventional cloud parameterizations with a cloud-resolving model (CRM) in each grid column of a GCM, constitutes a new and promising approach for climate modeling. The MMF can provide for global coverage and two-way interactions between the CRMs and their parent GCM. The CRM allows for explicit simulation of cloud processes and their interactions with radiation and surface processes, and the GCM allows for global coverage.

A new MMF has been developed that is based on the NASA Goddard Space Flight Center (GSFC) finite-volume GCM (fvGCM) and the Goddard Cumulus Ensemble (GCE) model. This Goddard MMF produces many features that are similar to another MMF that was developed at Colorado State University (CSU), such as an improved surface precipitation pattern, better cloudiness, improved diurnal variability over both oceans and continents, and a stronger propagating Madden-Julian oscillation (MJO) compared to their parent GCMs using traditional cloud parameterizations. Both MMFs also produce a large and positive precipitation bias in the Indian Ocean and western Pacific during the Northern Hemisphere summer. However, there are also notable differences between the two MMFs. For example, the CSU MMF simulates less rainfall over land than its parent GCM. This is why the CSU MMF simulated less overall global rainfall than its parent GCM. The Goddard MMF simulates more global rainfall than its parent GCM because of the high contribution from the oceanic component. A number of critical issues (i.e., the CRM's physical processes and its configuration) involving the Goddard MMF are discussed in this paper.