Abstract

To assess the impact of rainfall observations on short-range forecasts of precipitation, and to improve our understanding of the physical processes responsible for the development of a mesoscale convective system (MCS) associated with the dryline that occurred on 10 April 1979 in the midwestern United States, a series of four-dimensional variational data assimilation experiments was conducted based on the special dataset collected in the Severe Environmental Storm and Mesoscale Experiment. A nonhydrostatic mesoscale model (MM5) with a relatively simple moist physics and its adjoint were used for both the model simulation and data assimilation.

A previous numerical simulation of this MCS, based on conventional initialization procedures, failed to correctly simulate the location and intensity of the observed rainfall. This is attributed to the lack of mesoscale details in the model's initial conditions for the low-level moisture convergence and the upper-level disturbances related to the upper-level jet streak. In contrast, the initial conditions created by the four-dimensional variational data assimilation method, which incorporated 3-h rainfall data along with wind, temperature, surface moisture, and precipitable water measurements, produced an improved short-range (up to 12 h) rainfall prediction. It also captured many important mesoscale features including the structure of MCSs, the lower- and upper-level jets, the position of the dryline, the low-level moisture convergence, and the formation of a localized capping inversion (lid). In addition, the spinup time required for precipitation was reduced.

Additional experiments were conducted to assess the importance of lateral boundary conditions (LBCs) in the assimilation procedure, the importance of the precipitable water measurements, and the impact of moist physics. In comparison to the experiment in which only initial conditions (ICs) are used as a control variable, controlling both the initial and lateral boundary conditions during the minimization procedure produced a closer match to the observed rainfall while fewer changes are made to the analyzed ICs. The authors showed that assimilating precipitable water into the model is very important. The precipitable water assimilation constrains the large-scale model moisture error growth while allowing the model to generate mesoscale structures through rainfall assimilation. The 4DVAR rainfall assimilation experiments using two different cumulus parameterization schemes produced very similar adjustments to the original analysis, and model forecasts from the “optimal” ICs and LBCs obtained through rainfall assimilation using a cumulus parameterization scheme different from the one used in the 4DVAR procedure were seen to perform better than that from CTRL without 4DVAR.

These results strongly confirm that improved quantitative precipitation forecasts of mesoscale convective systems are possible through the assimilation of rainfall observations, along with other conventional data. Further improvement can be expected with the use of a high-resolution model with improved moist physics and boundary layer parameterization.

Abstract

In this paper, a variational data assimilation approach is used to assimilate the rain rate (RR) data together with precipitable water (PW) measurements from the Experiment on Rapidly Intensifying Cyclones over the Atlantic (ERICA) (4–5 January 1989; IOP-4 cyclone). The PW and RR, which are assimilated into the Pennsylvania State University–NCAR Mesoscale Model version 5 (MM5), are computed from the Special Sensor Microwave/Imager (SSM/I) raw data—brightness temperatures—via a statistical regression method. The SSM/I-derived RR and PW at 0000 UTC and/or 0930 UTC are assimilated into the MM5. The data at 2200 UTC are used for verification of the prediction results. Numerical experiments are performed using the MM5. Two horizontal resolutions of 50 km and 25 km are used in the authors’ studies. Comparisons are made between the experiments with and without SSM/I-measured PW and RR observations. Results from these experiments showed the following.

1) The MM5 simulated a well-behaved but slightly less intense, position-shifted cyclogenesis episode based on the NCEP analysis enhanced with only radiosonde and surface observations through a Cressman-type objective analysis.

2) The satellite-derived PW and RR observations were assimilated successfully into the MM5 model by a variational method. The cost function that measures the distance between the model-predicted and the observed PW and RR decreased by about one order of magnitude.

3) Assimilation of PW and RR significantly improved the cyclone prediction, reflected mostly in the cyclone’s track, the associated frontal structure and the associated precipitation along the front. The model’s spinup problem during the simulation was greatly reduced after assimilating the PW and RR information into the model initial conditions.

4) Sensitivity experiments of RR assimilation indicated that the impact on the results of RR assimilation was less sensitive to errors in the magnitude estimate than errors in the RR location.

5) It was shown that assimilation of RR only was not as effective in producing a satisfactory improvement on the cyclone prediction as the assimilation of both PW and RR. In addition, improvement in the cyclone prediction of RR assimilation was found to depend on the moist parameterization scheme since the cumulus parameterization resulted in a better 24-h cyclone forecast than the Kuo convective parameterization.

These results show that the SSM/I-measured PW and RR have great potential to improve the initial conditions for a mesoscale model, especially over the data-sparse oceanic regions. The case study carried out in this paper shows that the variational assimilation of SSM/I-measured PW and RR data produces adjustments in the model states and results in a positive impact on the forecast of the ERICA IOP-4 cyclone. Future experimentation is planned to assimilate the brightness temperature directly into a mesoscale model.

Abstract

The development of convection in the lower atmosphere created by diurnal heating of the earth's surface is investigated by the use of a nonlinear numerical model. It is found that under the influence of the stable stratification normally present at upper levels and with relatively strong convection, three distinctly different layers are always established, namely, a thin superadiabatic surface layer, a mixed layer, and a thin inversion layer which forms the base of a slightly modified upper stable layer. The temperature fluctuation has two maxima, one located just above the surface layer and the second in the inversion layer, while within the main body of the mixed layer the temperature fluctuation is minimal. On the other hand, the mean vertical velocity has its maximum in the middle of the mixed layer. Convection also penetrates far into the stable layer, but the horizontal cell size is much larger in the stable layer than in the mixed layer. Internal gravity waves are also set up by convection, with their maximum amplitude concentrated in the inversion layer just above the top of the mixed layer.

Comparison with observations shows that the results obtained from our numerical model are in good agreement with observations, including the change of the depth of the mixed layer with time. A simple formula for the forecasting of the mixed layer depth has also been developed.

Abstract

The influences of the Tibetian Plateau on the cumulative and diurnal changes of the meteorological fields in July are investigated by the use of a five-layer primitive equation model which includes the effects of solar and longwave radiation, cumulus convection, topography, internal and surface friction and a mean flow field. It is found that prominent diurnal variations in the meteorological fields are created by the special influence of the plateau on the distribution of solar energy. The vertical circulation so created is such that at 1800 LST at 90°E the motion is upward over the entire Plateau and its surroundings from the surface to the 100 mb level except in a very narrow region close to the eastern edge of the Plateau. At 0600 downward motion prevails over the Plateau and along the surrounding slopes up to the 300 mb level, but above 300 mb ascending motion still persists. The daily mean vertical circulation is characterized by ascending motion over the entire region of the Plateau and its surroundings, which is in general agreement with the mean July flow pattern obtained from observations by Yeh and Gao (1979).

In addition, the distribution of rainfall rate obtained from the simulation also is in fair agreement with the observed distribution in July, with cumulus rainfall contributing to more than three-fourths of the total rainfall in the tropics.

Abstract

In this study a nonlocal, linear observation operator for assimilating radio occultation data is evaluated. The operator consists of modeling the excess phase, that is, integrating the refractivity along straight lines tangent to rays, below a certain height. The corresponding observable is the excess phase integrated through the Abel-retrieved refractivity, along the same lines, below the same height. The operator allows very simple implementation (computationally efficient) while accurately accounting for the horizontal refractivity gradients. This is due to significant cancellation of the linearization and discretization errors when modeling the observable. Evaluation of the operator with Challenging Minisatellite Payload (CHAMP) radio occultation data and grid refractivity fields from high-resolution regional analysis over the continental United States showed reduction of the observation error in the troposphere (below 7 km) 1.5–2 times, compared to the error of local refractivity. The operator is useful for the assimilation of radio occultation data by high-resolution weather models in the troposphere.

Abstract

Recently it has been proposed that the phase delay associated with the radio signals propagating from GPS satellites to a ground-based GPS receiving station can be used to infer the vertically integrated water vapor (precipitable water—PW) with a high degree of accuracy. Since a ground-based GPS receiving station is relatively inexpensive, a specially designed, dense GPS network can provide PW measurements with unprecedented coverage. Such a data set can potentially have a significant impact on operational numerical weather prediction.

In this paper, a series of numerical experiments were conducted using a variational (4DVAR) data assimilation system based on The Pennsylvania State University –National Center for Atmospheric Research mesoscale model MM5 and its adjoint. The special soundings collected in SESAME (Severe Environmental Storms and Mesoscale Experiment) 1979 wore used in two sets of experiments. In the first set, a 1-h assimilation window and an analysis of the observed PW data were used. All data were assumed to be available at the end of the assimilation window. The assimilation of PW data was found to effectively recover the vertical structure of water vapor and improve the quality of moisture analysis. The use of surface humidity data in addition to PW analysis resulted in further improvement in the quality of the retrieved moisture fields, particularly in the lower troposphere. The assimilation of PW and surface humidity data reduced the rms errors in the initial moisture analysis by as much as 40%. Such improvement cannot be achieved by assimilation of wind and temperature data, because they do not carry sufficient information on the moisture field. The authors also found that the assimilation of PW and surface humidity data can lead to significant improvement in short-range precipitation forecasts when used along with the wind and temperature data. The use of PW and surface humidity data in 4DVAR increased the threat score from 0.01 to 0.48 for 3-h forecasts and from 0.43 to 0.65 for 6-h forecasts.

SESAME 1979 is a case with intensive convective activity, and the forecast is strongly affected by moist diabatic processes. The intent here is to test the impact of including adjoints of moist physics (adjoints of the Kuo cumulus convective scheme and the grid-resolvable precipitation) in the 4DVAR system to PW assimilation results during the initial stage of the storm case. Thus, the assimilation window is extended from 1 to 3 h, and it is assumed that PW data were available at an interval of 3 h in the second set of experiments. The PW data assimilated are generated by the model simulation. It was found that the inclusion of moist physics in the 4DVAR system reduced the systematic biases of the model, allowed a better fit between the model and observed data, and resulted in an improved “optimal” initial condition and, consequently, a better short-range prediction. The threat score was increased from 0.30 to 0.50 in the 6-h forecasts following the assimilation cycle. These results suggest that the effects of physical parameterization should be included in a 4DVAR data assimilation system, especially for a situation with significant precipitation over a relatively long assimilation window (greater than 3 h). The sensitivity of the 4DVAR results to the initial guess field was also tested. The results of 4DVAR were found to be relatively insensitive to the quality of the initial condition (the guess field). Even with a very poor initial moisture field, 4DVAR was able to produce a high quality moisture analysis after the PW data were assimilated, although the number of iterations required had to be increased from 30 to 50.

Abstract

Recently, a new approach to remote sensing of water vapor based on the Global Positioning System (GPS) has been proposed. Specifically, the bending of radio signals propagating from GPS satellites to a receiver on a low earth-orbiting satellite can be used to derive vertical profiles of atmospheric refractivity. Vertical profiles of temperature and water vapor can then be retrieved from the refractivity measurements. This is potentially a valuable data source for the meteorological community. However, before such measurements are used for operational numerical weather prediction, we need to assess the accuracy of the retrieved temperature and moisture fields and properly assimilate these observations into a numerical model.

A 4D data assimilation system based on the adiabatic version of the Penn State-NCAR Mesoscale Model and its adjoint was developed. A series of observing system simulation experiments was then conducted to assess the impact of GPS-derived atmospheric refractivity data. Specifically, a 20-km simulation of a winter storm in March 1992 over the continental United States was used as the control experiment. Vertical profiles of atmospheric refractivity were extracted from the control simulation at selected temporal and spatial resolutions. The simulated GPS measurements were then assimilated into a 60-km MM5 using the 4D variational data assimilation approach. The results showed that the assimilation of atmospheric refractivity is very effective in recovering the vertical profiles of water vapor. The accuracy of the derived water vapor field is significantly better than that obtained through the traditional retrieval technique. The assimilation of atmospheric refractivity is also shown to provide useful temperature information.

The data assimilation results are relatively insensitive to the random errors added to the simulated refractivity observations. However, they are very sensitive to the spatial resolution of the observations. A spectral analysis shows that the moisture field has more small-scale variation and is more sensitive to the resolution of the atmospheric refractivity observations than the temperature. When refractivity observations are available on a coarser resolution, assimilation of individual observations produces better results than assimilation of the interpolated observations on the model grid. Assimilation of the refractivity observations averaged along a distance of about 240 km, which represents the characteristic scale of the GPS refractivity measurement, still produces reasonably good results for the retrieval of temperature and moisture fields.

Abstract

Significant progress has been made in the short-range (1–2 days) prediction of east coast cyclogenesis over the past decade. This is the result of improved model resolution, physical parameterization, and good analysis of the upstream conditions often well sampled by the high-density North America observing network. The prediction of cyclogenesis over the eastern Pacific Ocean, or a longer-range forecast of east coast cyclones, does not share the same degree of success, largely due to the fact that the upstream conditions fall over the data-void regions of the Pacific Ocean. In this paper, the authors study the prediction of the ERICA IOP-4 storm using a 120-km hemispheric version of the Penn State/NCAR Mesoscale Model MM5, with forecast duration ranging from 36 to 120 h. Specifically, the impact of uncertainties in the initial conditions on the 5-day forecastof this cyclogenesis event was examined. Initial perturbations were then introduced to the original analysis at initial time based on the 12-h forecast errors. Results from the numerical experiments led to the following conclusions.

• The skill of the model forecasts degraded as the forecast duration was lengthened. The model was able to capture the cyclogenesis up to 4.5 days in advance. Significant degradation occurred between day 4.5 and day 5, and the 5-day forecast failed to predict a major cyclone over the western Atlantic Ocean.

• Initial perturbations, determined by minimizing the errors of the initial 12-h forecast and introduced to the original analysis, were shown to improve the 5-day forecast substantially. Most remarkably, the 5-day forecast using the perturbed initial condition performed better than a forecast that was initialized 12 h later.

• Analysis of the derived initial perturbation showed that the main uncertainties in the initial condition were related to 1) the lower-tropospheric temperature analysis over the southern Rocky Mountain and Mexico regions and 2) the description of the upper-level potential vorticity (PV) anomaly over the Gulf of Alaska. The latter is partially related to the model’s systematic bias errors over that region.

• Sensitivity experiments carried out by adding the initial perturbation only to limited regions on selected model variables showed that the modification of the upper-level PV anomaly in itself was not sufficient to improve the 5-day forecast substantially. In contrast, the perturbation made to the low-tropospheric temperature field was critical for forecast improvement, by changing the patterns of thickness advection and structure of a shortwave trough involved in the cyclone development.

The results presented in this paper suggest that it may be possible to improve the skill of medium-range forecasts of certain types of east coast cyclogenesis (such as the IOP-4 storm studied in this paper) if both (i) the quality of model initial conditions over key data-sparse regions (such as the eastern Pacific and Rocky Mountain regions) and (ii) the accuracy of short-term forecasting can be improved.

Abstract

Thermodynamic states in clouds are closely related to physical processes such as phase changes of water and longwave and shortwave radiation. Global Positioning System (GPS) radio occultation (RO) data are not affected by clouds and have high vertical resolution, making them ideally suited to cloud profiling on a global basis. By comparing the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) RO refractivity data with those of the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis and ECMWF analysis for soundings in clouds and clear air separately, a systematic bias of opposite sign was found between large-scale global analyses and the GPS RO observations under cloudy and clear-sky conditions. As a modification to the standard GPS RO wet temperature retrieval that does not distinguish between cloudy- and clear-sky conditions, a new cloudy retrieval algorithm is proposed to incorporate the knowledge that in-cloud specific humidity (which affects the GPS refractivities) should be close to saturation. To implement this new algorithm, a linear regression model for a sounding-dependent relative humidity parameter α is first developed based on a high correlation between relative humidity and ice water content. In the absence of ice water content information, α takes an empirical value of 85%. The in-cloud temperature profile is then retrieved from GPS RO data modeled by a weighted sum of refractivities with and without the assumption of saturation. Compared to the standard wet retrieval, the cloudy temperature retrieval is consistently warmer within clouds by ∼2 K and slightly colder near the cloud top (∼1 K) and cloud base (1.5 K), leading to a more rapid increase of the lapse rate with height in the upper half of the cloud, from a nearly constant moist lapse rate below and at the cloud middle (∼6°C km⁻¹) to a value of 7.7°C km⁻¹, which must be closer to the dry lapse rate than the standard wet retrieval.

Abstract

On 19 September 1996, a squall line stretching from Nebraska to Texas with intense embedded convection moved eastward across the Kansas–Oklahoma area, where special observations were taken as part of a Water Vapor Intensive Observing Period sponsored by the Atmospheric Radiation Measurement program. This provided a unique opportunity to test mesoscale data assimilation strategies for a strong convective event. In this study, a series of real-data assimilation experiments is performed using the MM5 four-dimensional variational data assimilation (4DVAR) system with a full physics adjoint. With a grid size of 20 km and 15 vertical layers, the MM5-4DVAR system successfully assimilated wind profiler, hourly rainfall, surface dewpoint, and ground-based GPS precipitable water vapor data. The MM5-4DVAR system was able to reproduce the observed rainfall in terms of precipitation pattern and amount, and substantially reduced the model errors when verified against independent observations.

Additional data assimilation experiments were conducted to assess the relative importance of different types of mesoscale observations on the results of assimilation. In terms of the assimilation model’s ability to recover the vertical structure of moisture and in reproducing the rainfall pattern and amount, the wind profiler data have the maximum impact. The ground-based GPS data have a significant impact on the rainfall prediction, but have relatively small influence on the recovery of moisture structure. On the contrary, the surface dewpoint data are very useful for the recovery of the moisture structure, but have relatively small impact on rainfall prediction. The assimilation of rainfall data is very important in preserving the precipitation structure of the squall line. All the data are found to be useful in this mesoscale data assimilation experiment.

Issues related to the assimilation time window, weighting of different types of observations, and the use of accurate observation operator are also discussed.