Abstract

A new parameterization has been developed that assumes that nonprecipitating particles obey the Heymsfield–Platt power-law (H–P particles) and that the precipitating particles obey the Marshall–Palmer distribution (M–P particles). The parameterization defines a critical ice content for the onset of precipitation particles and allows the number of ice crystals, the extinction coefficient, and the effective diameter of the crystals for the cloud layer in the model to be diagnosed. The implementation of the new parameterization in a model unifies the microphysical assumptions used to calculate the optical properties and precipitation.

If it is assumed that the number of H–P particles at cloud top is much larger than the number of M–P particles in southeastern Australia frontal systems, then the observed number of ice crystals at cloud top agrees well with the diagnosed number of H–P particles at cloud top.

A simulation of the passage of a cold front is used to test the parameterization. The modeled H–P and M–P particle concentrations are compared with microphysical observations of ice crystal concentrations and the modeled optical depths are compared with International Satellite Cloud Climatology Project satellite data. The modeled cloud ice contents and the particle numbers (H–P particles + M–P particles) in the middle-level frontal cloud are consistent with the ice crystal numbers that are observed in these types of clouds. The model simulations show that when the front is in the mature stage of development, the model-derived optical depths are in reasonable agreement with those derived from satellites.

From 1947 to 1994 a number of cloud-seeding experiments were done in Australia based on the static cloud-seeding hypothesis. A critical analysis of these successive cloud-seeding experiments, coupled with microphysical observations of the clouds, showed that the static cloud-seeding hypothesis is not effective in enhancing winter rainfall in the plains area of Australia. However, there is evidence to suggest that cloud seeding is effective for limited meteorological conditions in stratiform clouds undergoing orographic uplift. In particular, two successive experiments in Tasmania show strong statistical evidence for rainfall enhancement when cloud-top temperatures are between −10° and −12°C in a southwesterly stream. The evidence for similar effects on the Australian mainland is more controversial. In the summer rainfall regions of northern Australia, the extreme rainfall variability makes it impossible to design a statistical experiment that can to be evaluated in a reasonable time using currently available techniques. Rainfall enhancement in these regions remains inconclusive.

Abstract

Idealized tropical cyclones are inserted into a regional climate model and the resulting intensity evolution of the storms is examined under current and enhanced greenhouse climates. The regional climate model is implemented over a model domain near Australia. In general, storm intensities increase under enhanced greenhouse conditions, although these increases are mostly not statistically significant. The simulated intensities are compared to theoretically derived estimates of maximum potential intensity. The theoretical estimates are mostly larger than the simulated intensities, suggesting that other factors may be limiting the intensification of the storms. Two such factors are suggested: the limited horizontal resolution of the storm simulations and the presence of vertical wind shear. Significant regression relations are demonstrated between maximum intensity of the simulated storms as predicted by sea surface temperature and vertical wind shear variations, while much weaker relationships are shown between maximum intensity and sea surface temperature alone. This suggests that dynamical influences such as vertical wind shear, which are not included in theoretical estimates of maximum potential intensity, act to restrict the development of the storm and thereby its maximum intensity.

Abstract

A scheme for calculation of the liquid fraction f _l in mixed-phase stratiform clouds has been developed for use in large-scale models. An advantage of the scheme, compared to the interpolation in temperature that is typically used, is that it makes it possible to simulate the life cycles of mixed-phase clouds, and the differences between deep and shallow clouds. The central part of the scheme is a physically based calculation of the growth of cloud ice crystals by vapor deposition at the expense of coexisting cloud liquid water, the so-called Bergeron–Findeisen mechanism. Versions of this calculation have been derived for three different ice-crystal habits (spheres, hexagonal plates, or columns) and for two different assumed spatial relationships of the coexisting cloud ice and liquid water (horizontally adjacent or uniformly mixed). The scheme also requires a parameterization of the ice crystal number concentration N _i .

The variation with temperature of f _l looks broadly realistic compared to aircraft observations taken in the vicinity of the British Isles when the scheme is used in the CSIRO GCM, if N _i is parameterized using a supersaturation-dependent expression due to Meyers et al. If Fletcher’s earlier temperature-dependent expression for N _i is used, the scheme generates liquid fractions that are too large. There is also considerable sensitivity to the ice-crystal habit, and some sensitivity to model horizontal resolution and to the assumed spatial relationship of the liquid water and ice. A further test shows that the liquid fractions are lower in cloud layers that are seeded from above by falling ice, than in layers that are not seeded in this way.

The scheme has also been tested in limited-area model simulations of a frontal system over southeastern Australia. Supercooled liquid water forms initially in the updraft, but mature parts of the cloud are mostly glaciated down to the melting level. This behavior, which is considered to be realistic based on observations of similar cloud systems, is not captured by a conventional temperature-dependent parameterization of f _l . The variation with temperature of f _l shows a marked sensitivity to the assumed spatial relationship of the liquid water and ice. The results obtained using the uniformly mixed assumption are considered to be more realistic than those obtained using the horizontally adjacent assumption. There is also much less sensitivity to the time step when the former assumption is used.

Abstract

Gray's seasonal genesis parameter (SGP) is reassessed as a diagnostic quantity for both climatological and single-season tropical cyclogenesis. The SGP applied to global analyses from recent years is able to locate the regions of genesis activity during 1967–86. The SGP based on the climatology of a simulation by the CSIR09 atmospheric model using prescribed ocean temperatures for 1979–88 has similar skill. The SGP applied to single-season means is then assessed as a diagnostic for interannual variation of cyclogenesis. Increased cyclogenesis in the central Pacific during the 1982/83 El Niño coincides with increased SGP. CSIRO9 simulated similar variations in the SGP. Moderate correlations are found between the time series of the observed and inferred simulated cyclogenesis numbers in the central Pacific, eastern North Pacific, and North Atlantic regions during 1979–88. However, elsewhere the correlations were poor.

Abstract

It is commonly accepted that there is a monotonically increasing relationship between sea surface temperature (SST) and tropical cyclone intensity (as measured by maximum near-surface winds or minimum central pressure). This perceived relationship has been used to extrapolate the effects of climatologically warmer SSTs on tropical cyclones These warmer SSTs are one of the consequences of doubled C0₂ predicted by climate general circulation models (GCMs). Very few investigations have actually critically addressed this SST-storm intensity relationship, however. In this paper, a limited area modeling study is used to explore the potential links between SST and tropical cyclone intensity. Previous work, including some observational data, is reviewed and its implications for the interpretation of the results given here is presented. Finally, the implications of the changes in SST on the thermodynamic structure of the atmosphere-in particular, the destabilization of the boundary layer-are identified as another possible mechanism of intensification for these modeled storms.

Abstract

The Haines index is used in wildfire forecasting and monitoring to evaluate the potential contributions of atmospheric stability and humidity to the behavior of plume-dominated wildfires. The index has three variants (“low,” “mid,” and “high”) that accommodate differences in surface elevation. As originally formulated, the low variant is calculated from temperature observations at the 950- and 850-hPa levels and humidity observations at 850 hPa. In the early 1990s the National Weather Service implemented a new mandatory level for radiosonde observations at 925 hPa. Following this change, measurements at 950 hPa became less frequent. An informal survey of several forecast offices found no formalized adjustment to the calculation of the low Haines index to take into account the nonavailability of 950-hPa measurements. Some sources continue to use 950-hPa temperature, usually interpolated from 925-hPa and surface temperatures, to calculate the low Haines index. Others directly substitute the 925-hPa temperature for the originally specified 950-hPa value. This study employs soundings from the central United States when both 950- and 925-hPa levels were available to investigate the impact of different calculation approaches on the resulting values of the low variant of the Haines index. Results show that direct substitution of 925-hPa temperature for the 950-hPa temperature can dramatically underestimate the potential wildfire severity compared with the original formulation of the Haines index. On the other hand, a low-elevation variant of the Haines index calculated from the interpolated 950-hPa temperature is usually in close agreement with the original formulation of the index.

Abstract

Satellites provide the largest dataset for monitoring the earth system and constraining analyses in numerical weather prediction models. A significant challenge for utilizing satellite radiances is the accurate estimation of their biases. High-accuracy non-radiance data are commonly employed to anchor radiance bias corrections. However, aside from the impacts of radio occultation data in the stratosphere, the influence of other types of “anchor” observation data on radiance assimilation remain unclear. This study provides an assessment of impacts of dropsonde data collected during the Atmospheric River (AR) Reconnaissance program, which samples ARs over the Northeast Pacific, on the radiance assimilation using the Global Forecast System (GFS) and Global Data Assimilation System at National Centers for Environmental Prediction.

The assimilation of this dropsonde dataset has proven crucial for providing enhanced anchoring for bias corrections and improving the model background, leading to an increase of ~5–10% in the number of assimilated microwave radiance in the lower/middle troposphere over the Northeast Pacific and North America. The impact on tropospheric infrared radiance is small but also beneficial. Impacts of dropsondes on the use of stratospheric channels are minimal due to the absence of dropsonde observations at certain altitudes, such as aircraft flight levels (e.g., 150 hPa). Results in this study underscore the usefulness of dropsondes, along with other conventional data, in optimizing the assimilation of satellite radiance. This study reinforces the importance of a diverse observing network for accurate weather forecasting and highlights the specific benefits derived from integrating dropsonde data into radiance assimilation processes.

Abstract

Satellites provide the largest dataset for monitoring the earth system and constraining analyses in numerical weather prediction models. A significant challenge for utilizing satellite radiances is the accurate estimation of their biases. High-accuracy nonradiance data are commonly employed to anchor radiance bias corrections. However, aside from the impacts of radio occultation data in the stratosphere, the influence of other types of “anchor” observation data on radiance assimilation remains unclear. This study provides an assessment of impacts of dropsonde data collected during the Atmospheric River (AR) Reconnaissance program, which samples ARs over the northeast Pacific, on the radiance assimilation using the Global Forecast System (GFS) and Global Data Assimilation System at the National Centers for Environmental Prediction. The assimilation of this dropsonde dataset has proven crucial for providing enhanced anchoring for bias corrections and improving the model background, leading to an increase of ∼5%–10% in the number of assimilated microwave radiance in the lower troposphere/midtroposphere over the northeast Pacific and North America. The impact on tropospheric infrared radiance is not only small but also beneficial. Impacts of dropsondes on the use of stratospheric channels are minimal due to the absence of dropsonde observations at certain altitudes, such as aircraft flight levels (e.g., 150 hPa). Results in this study underscore the usefulness of dropsondes, along with other conventional data, in optimizing the assimilation of satellite radiance. This study reinforces the importance of a diverse observing network for accurate weather forecasting and highlights the specific benefits derived from integrating dropsonde data into radiance assimilation processes.

Abstract

Atmospheric River Reconnaissance (AR Recon) is a targeted campaign that complements other sources of observational data, forming part of a diverse observing system. AR Recon 2021 operated for ten weeks from January 13 to March 22, with 29.5 Intensive Observation Periods (IOPs), 45 flights and 1142 successful dropsondes deployed in the northeast Pacific. With the availability of two WC-130J aircraft operated by the 53^rd Weather Reconnaissance Squadron (53 WRS), Air Force Reserve Command (AFRC) and one National Oceanic and Atmospheric Administration (NOAA) Aircraft Operations Center (AOC) G-IVSP aircraft, six sequences were accomplished, in which the same synoptic system was sampled over several days.

The principal aim was to gather observations to improve forecasts of landfalling atmospheric rivers on the U.S. West Coast. Sampling of other meteorological phenomena forecast to have downstream impacts over the U.S. was also considered. Alongside forecast improvement, observations were also gathered to address important scientific research questions, as part of a Research and Operations Partnership.

Targeted dropsonde observations were focused on essential atmospheric structures, primarily atmospheric rivers. Adjoint and ensemble sensitivities, mainly focusing on predictions of U.S. West Coast precipitation, provided complementary information on locations where additional observations may help to reduce the forecast uncertainty. Additionally, Airborne Radio Occultation (ARO) and tail radar were active during some flights, 30 drifting buoys were distributed, and 111 radiosondes were launched from four locations in California. Dropsonde, radiosonde and buoy data were available for assimilation in real-time into operational forecast models. Future work is planned to examine the impact of AR Recon 2021 data on model forecasts.