Search

FORMOSAT-7/COSMIC-2 radio occultation (RO) measurements show promise for observing the deep troposphere and for providing critical information on the Earth's planetary boundary layer (PBL). However, refractivity retrieved in the low troposphere can have severe biases under certain thermodynamic conditions. This research examines the characteristics of the deep tropospheric biases and presents methods for estimating the region-dependent refractivity bias using statistical regression models. The results show that the biases have characteristics that vary over land and oceans. With substantial correlation between local spectral width (LSW) and bias, the LSW-based bias estimation model can explain the general pattern of the refractivity bias but with deficiencies in measuring the bias in the ducting regions and in certain areas over land. The estimation model involving the relationship with temperature and specific humidity (TQ) can capture the large biases associated with ducting. Finally, a minimum variance estimation that combines the LSW and TQ provides the most accurate estimation of the refractivity bias. [ABSTRACT FROM AUTHOR]

Studies have shown that assimilating the radio occultation (RO) observations, including those from the FORMOSAT‐3/COSMIC (constellation observing systems for meteorology, ionosphere, and climate) (FS3‐C), provides positive impacts on tropical cyclone (TC) forecasts. The FS3‐C's successor, the FORMOSAT‐7/COSMIC‐2 (FS7‐C2), provides denser spatial data coverage over the Tropics and Subtropics, where severe weather systems often occur. This study investigates the impact of FS7‐C2 refractivity profiles on the prediction of TC genesis. A quick observing system simulation experiment is conducted for the period when Hurricanes Helene and Gordon (2006) occurred over the North Atlantic Ocean using a regional ensemble data assimilation system. Though assimilating FS3‐C or FS7‐C2 ROs successfully reproduces Helene's development, assimilating FS7‐C2 ROs better captures the genesis and development of Gordon with abundant moisture and vorticity in Gordon's core region, providing conditions favorable for the development of deep convection. A minimum area‐mean total precipitable water vapor of 54 mm, as well as the existence of mid‐level cyclonic vorticity (e.g., 500 hPa), at the storm core region in the initial condition is required for forecasting Gordon's genesis. Also, the assimilation of FS7‐C2 ROs in our experiments reduces the 500 hPa geopotential error by 22% and improves probabilistic quantitative precipitation forecast compared with assimilating FS3‐C ROs. Two sensitivity tests are conducted to evaluate the impact of low‐level negatively biased FS7‐C2 RO profiles and the removal of FS7‐C2 data below 3 km on Gordon's genesis. The former test does not degrade Gordon's genesis forecast skills due to a dipole error correlation between the background ROs and the moisture field over an observed RO profile near Gordon. The latter test does degrade Gordon's forecast skills but is still better than the assimilation of FS3‐C ROs since the features of low‐level moisture and mid‐level vorticity are preserved to some extent. [ABSTRACT FROM AUTHOR]

The potential use of chaos synchronization techniques in data assimilation for numerical weather prediction models is explored by coupling a Lorenz three-variable system that represents 'truth' to another that represents 'the model.' By adding realistic 'noise' to observations of the master system, an optimal value of the coupling strength was clearly identifiable. Coupling only the y variable yielded the best results for a wide range of higher coupling strengths. Coupling along dynamically chosen directions identified by either singular or bred vectors could improve upon simpler chaos synchronization schemes. Generalized synchronization (with the parameter r of the slave system different from that of the master) could be easily achieved, as indicated by the synchronization of two identical slave systems coupled to the same master, but the slaves only provided partial information about regime changes in the master. A comparison with a standard data assimilation technique, three-dimensional variational analysis (3DVAR), demonstrated that this scheme is slightly more effective in producing an accurate analysis than the simpler synchronization scheme. Higher growth rates of bred vectors from both the master and the slave anticipated the location and size of error spikes in both 3DVAR and synchronization. With less frequent observations, synchronization using time-interpolated observational increments was competitive with 3DVAR. Adaptive synchronization, with a coupling parameter proportional to the bred vector growth rate, was successful in reducing episodes of large error growth. These results suggest that a hybrid chaos synchronization-data assimilation approach may provide an avenue to improve and extend the period for accurate weather prediction.

A mesoscale convective system that occurred in southwestern Taiwan on 15 June 2008 is simulated using convection-allowing ensemble forecasts to investigate the forecast uncertainty associated with four microphysics schemes—the Goddard Cumulus Ensemble (GCE), Morrison (MOR), WRF single-moment 6-class (WSM6), and WRF double-moment 6-class (WDM6) schemes. First, the essential features of the convective structure, hydrometeor distribution, and microphysical tendencies for the different microphysics schemes are presented through deterministic forecasts. Second, ensemble forecasts with the same initial conditions are employed to estimate the forecast uncertainty produced by the different ensembles with the fixed microphysics scheme. GCE has the largest spread in most state variables due to its most efficient phase conversion between water species. By contrast, MOR results in the least spread. WSM6 and WDM6 have similar vertical spread structures due to their similar ice-phase formulas. However, WDM6 produces more ensemble spread than WSM6 does below the melting layer, resulting from its double-moment treatment of warm rain processes. The model simulations with the four microphysics schemes demonstrate upscale error growth through spectrum analysis of the root-mean difference total energy (RMDTE). The RMDTE results reveal that the GCE and WDM6 schemes are more sensitive to initial condition uncertainty, whereas the MOR and WSM6 schemes are relatively less sensitive to that for this event. Overall, the diabatic heating–cooling processes connect the convective-scale cloud microphysical processes to the large-scale dynamical and thermodynamical fields, and they significantly affect the forecast error signatures in the multiscale weather system. [ABSTRACT FROM AUTHOR]

The representation of model error in ensemble prediction systems (EPSs) can be limited by the assumptions within parameterization schemes. Stochastic perturbed parameterization tendencies (SPPT) is one representation of model error that randomly perturbs parameterized physical tendencies using a spatially and temporally correlated red-noise field. This research investigates the sensitivity of ensemble rainfall forecasts produced by the Weather Research and Forecasting (WRF) Model to the configuration of SPPT and independent SPPT (iSPPT) for three meso–synoptic-scale heavy rainfall events over the United States and Taiwan, primarily focusing on the ensemble mean and standard deviation as well as forecast skill. Thirty-two 20-member ensembles, which represent a combination of eight configurations of the stochastic perturbation time scale, length scale, and amplitude scale, and four perturbed parameterization schemes, as well as an unperturbed control simulation, are examined for each event. In each case, rainfall standard deviation is most sensitive to the perturbation time scale and amplitude scale. Moreover, microphysics tendency perturbations are associated with the largest standard deviation in two of the three events, followed by perturbations to the total (nonmicrophysics), turbulent mixing, and radiation parameterized tendencies. Additionally, microphysics tendency perturbations are associated with an increase in the areal coverage of heavy rainfall compared to the control forecast, regardless of whether the control forecast over or underrepresents the observed rainfall distribution. [ABSTRACT FROM AUTHOR]

We test an ensemble data assimilation system using the four‐dimensional Local Ensemble Transform Kalman Filter (4D‐LETKF) for a global numerical weather prediction (NWP) model with unstructured grids on the cubed‐sphere. It is challenging to selectively represent structures of dynamically growing errors in background states under system uncertainties such as sampling and model errors. We compute Ensemble Singular Vectors (ESVs) in an attempt to capture fast‐growing errors on the subspace spanned by ensemble perturbations, and use them as additive inflation to enlarge the covariance in the area where errors are flow‐dependently growing. The performance of the 4D‐LETKF system with ESVs is evaluated in real data assimilation, as well as Observing System Simulation Experiments (OSSEs). We find that leading ESVs help to capture fast‐growing errors effectively, especially when model errors are present, and that the use of ESVs as additive inflation significantly improves the performance of the 4D‐LETKF. Background error (contours, solid: positive, dash: negative) and analysis increment (shading) of zonal wind U (m/s) at the model level 45, around 850 hPa at 0600 UTC 20 February 2014 from the tests (a) ImPerfModel‐Random, and (b) ImPerfModel‐ESV. [ABSTRACT FROM AUTHOR]