Abstract

The paper describes the results of an experiment where, for a series of flights, Philips RS4 and Väisälä RS80 radiosondes were mounted on the same balloon. It is shown that there are both random and systematic differences in the raw and derived data generated from these systems. At all levels above 1000 hPa, solar corrected RS4 temperature soundings are colder than those of the RS8O; resulting in a geopotential height difference of the order of 90 m at 50 hPa. The Väisälä RS8O Omega winds are similar to radar-derived wind profiles except in regions of changing vertical shear.

Abstract

The retrieval of vertical profiles of temperature and water vapor from atmospheric radiances is an ill-posed, nonlinear inversion problem. A linear retrieval estimator must be cast in a form which both minimizes the effects of unmodeled nonlinear processes, and provides retrieval constraints that are pertinent to the sounded atmospheres.

Here, the ill-posed aspect of the problem is resolved by defining a set of meteorologically reasonable retrieval estimator constraints through typical shape function (TSF) classification of a large sample of radiosonde observations. The companion problem of discriminating the TSF constraints to be applied to a particular retrieval estimator, given a set of observed radiances, is investigated. Since the particular linear model chosen to represent the radiance measurements will also have some impact on the retrieval estimator, the effects of errors arising from both simple and simultaneous linearization models for the radiative transfer equation are examined. A TSF constrained, simultaneous, maximum a posteriori retrieval estimator is formulated. Also, a classified, single field-of-view, cloud detection and clear radiance estimator is developed for overcast soundings.

The fundamental properties of the new retrieval estimator are examined and specified via synthetic TOVS radiance data experiments. The retrieval algorithm is also applied to two successive NOAA-7 passes over the New Zealand region, and the retrievals compared with those from a regression retrieval scheme, and operational NWP analysis fields.

Abstract

Temperature sensors on radiosondes measure a temperature that is a balance between the temperature of the air and the temperature of the radiation environment of the sensor. Because of this balance, the temperature reported by a radiosonde differs from the true air temperature by an amount that is determined by the heat-transfer coefficient, the longwave emissivity of the sensor, the shortwave emissivity, the longwave flux on the sensor surface, the shortwave flux on the sensor surface, and the sensor temperature. Of these quantities, the heat-transfer coefficient is determined by properties of both the sensor and the atmosphere, the reflectivities are determined by the sensor, and the fluxes and air temperature are determined by the atmosphere. For a typical radiosonde, the radiative properties of the sensor can be determined, the coefficient of heat transfer can be estimated, and models exist for calculating the shortwave flux. In this paper, the authors show that a modification of the Elsasser formulation for infrared fluxes can be used to calculate the infrared flux. This provides sufficient information to solve for the temperature difference between the temperature sensor and the air. The method is used to calculate errors for some typical meteorological conditions for the white-coated VIZ sensor, made by VIZ Manufacturing Co.

The method was used to examine the range of radiation errors for typical conditions. Although the shortwave radiation error is generally recognized because it is observed in the day-to-night differences, it is demonstrated that the longwave radiation errors are significant and variable. The longwave error for the VIZ instrument can reach 3 K at 10 hPa for a winter profile and can exceed that when there is a stratospheric warming. While the longwave radiation error is generally considered to be a cooling effect, at 100 hPa the longwave radiation is a source of heating in a tropical atmosphere because the tropopause at 100 hPa is cold relative to the rest of the atmosphere that is radiating to the sensor. Clouds have significant effects on both the longwave and shortwave components. A cloud at the tropopause of a tropical atmosphere can change the error of a radiosonde above the cloud from a heating error to a cooling error. At sunrise and sunset, the change in the shortwave error is abrupt. Extremely accurate knowledge of the time and location of the radiosonde is required to reduce the uncertainty in temperature to less than 1 K at the upper levels at these times.

Abstract

Twelve months of Southern Hemisphere (maritime) midlatitudes Advanced Very High Resolution Radiometer local area coverage data at full radiometric and spatial resolution have been collocated with rain-rate data from three Doppler weather radars.

Using an interactive computing environment, large independent samples of cloudy-altocumulus, cumulonimbus, cirrostratus, cumulus, nimbostratus, stratocumulus, stratus-and cloud-free scenes have been identified (labeled) in the collocated data. Accurate labeling was ensured by providing a supervising-analyst access to appropriate diagnostics, including difference and ratio channels, 3.7-µm reflected and emissive components, spectral histograms, Coakley-Bretherton spatial coherence plots, mean, standard deviation, and gray-level difference (GLD) statistics. This analysis yielded 4323 cloud and no-cloud samples at a spatial resolution of 8 × 8 instantaneous fields of view (IFOV), from 257 NOAA-11 and NOAA-12 orbits.

Bayesian cloud discriminant functions calculated from the labeled samples and utilizing feature vectors including radiometric and GLD spatial characteristics successfully classified scenes into one of the seven cloud and no-cloud classes with significant skill (Kuipers’ performance index 0.63). Utilizing the posterior probability of the classified samples enabled some clouds that were classified erroneously to be identified (and discarded), improving the skill of the discriminant functions by an additional 10% or so. Removing the GLD statistics from the feature vector reduced the skill of the cloud discrimination by about 20% (relative to the nondiscarding discriminant function), while increasing the misclassification of midlevel clouds. However, some cloud classes can only be discriminated from their multispectral signatures. Day and night discriminant functions show similar skill.

Within raining cloud classes, rain rate has been related to the spatial and radiometric characteristics of the cloud. The skill of the rain-rate estimates is dependent on the cloud type. For nimbostratus and altocumulus classes 20%–25% of the rain-rate variation can be explained by predictors that measure the temperature, spatial texture, and degree of isotropy in the sampled clouds. Raining and nonraining samples of altocumulus, cumulus, cirrostratus, and nimbostratus can be delineated with at least 60% accuracy.

This approach, whereby cloud classes are identified then rain rates estimated as a function of cloud type, would seem to resolve some of the usual problems associated with rain-rate analyses from midlatitudes infrared and visible satellite data. It also extends rain-rate diagnosis to nonconvective (frontal) cloud systems.

Abstract

Strong southerly winds regularly occur in the Cook Strait region of New Zealand. Occasionally, these winds are strong enough to cause severe damage to property and threaten human life. One example of a storm containing such winds is the “Wellington Storm,” which occurred on 20 June 2013. For this case, wind speeds in Cook Strait were stronger than those observed or forecast elsewhere in the storm. Even though wind speeds of this intensity are rare, storms affecting New Zealand with central pressures equal to the Wellington Storm (~976 hPa) are not uncommon. Numerical experiments have been carried out to investigate the possible reasons for the exceptional damaging southerly winds (DSWs) occurring in this storm. Analyses of the simulations showed that DSWs in Cook Strait for this event were actually barrier jets, not gap winds as they appeared. The strength of barrier jets in Cook Strait is sensitive to the precise location of the storm center. This explains the uncommon occurrence of DSWs in Cook Strait. Numerical experiments that used scaled (either increased or decreased) New Zealand orography showed that the barrier jets became shallower and weaker when the mountain top heights were lower. This decrease in barrier jet strength with mountain height is largely consistent with the results from linear-scale analyses in previous publications. This result implies that numerical simulations using a lower topography than actual (usually the case in current operational NWP) may lead to errors in timing and in forecasting the strength of the damaging winds associated with barrier jets.

Abstract

The fire danger rating system implemented in New Zealand is the Canadian Fire Weather Index (FWI) System developed 40 years ago for Canadian temperate forests. Issues have been raised in relation to this system when applied in other regions with different climate and vegetation environments. For the first time, two methods were proposed for improving the Drought Code (DC) component of the FWI System for New Zealand. The first method (PotE) employs a potential evaporation (PE) scheme that considers wind speed, surface air stability, and water vapor mixing ratio gradient. The second method (soilM) uses soil moisture. For the latter, when soil moisture is derived from observations, the calculated DC represents the actual drought status of the soil. DC and FWI have been calculated with the original and the two new DC methods at 28 climate stations in New Zealand for a pair of 2-yr periods. The Joint U.K. Land Environment Simulator (JULES) was run to provide the PE and soil moisture for the two methods. The original DC method underestimated the drought status in New Zealand, especially in summer, leading to underestimation of FWI. The PotE method significantly overestimated the drought status in summer. The errors in the calculated drought status and FWI were largely reduced by using the soilM method with simulated soil moisture from JULES. In this paper, the reasons for this reduction in error are investigated by testing the sensitivity of DC to surface evaporation and to soil parameters. Potential benefit is found from using the proposed soilM method for monitoring drought status and for FWI calculations.

Abstract

Historically most soil moisture–land surface impact studies have focused on continents because of the important forecasting and climate implications involved. For a relatively small isolated mountainous landmass in the ocean such as New Zealand, these impacts have received less attention. This paper addresses some of these issues for New Zealand through numerical experiments with a regional configuration of the Met Office Unified Model atmospheric model. Two pairs of idealized simulations with only contrasting dry or wet initial soil moisture over a 6-day period in January 2004 were conducted, with one pair using realistic terrain and the other pair flat terrain.

For the mean of the 6 days, the differences in the simulated surface air temperature between the dry and moist cases were 3–5 K on the leeside slopes and 1–2 K on the windward slopes and the central leeside coastal region of the South Island in the afternoon. This quite nonuniform response in surface air temperature to a uniformly distributed soil moisture content and soil type is mainly attributed to modification of the effects of soil moisture by mountains through two different processes: 1) spatial variation in cloud coverage across the mountains ranges leading to more shortwave radiation at ground surface on the leeside slope than the windward slope, and 2) the presence of a dynamically and thermally induced onshore flow on the leeside coast bringing in air with a lower sensitivity to soil moisture.

The response of local winds to soil moisture content is through direct or indirect effects. The direct effect is due to the thermal contrast between land and sea/land shown for the leeside solenoidal circulations, and the indirect effect is through the weakening of the upstream blocking of the South Island for dryer soils shown by the weakening and onshore shift of the upstream deceleration and forced ascent of incoming airflow.

Abstract

This paper reports on the first application of a multispectral textural Bayesian cloud classification algorithm (“SRTex”) to the general problem of the determination of high–spatial resolution cloud-amount and cloud-type climatological distributions. One year of NOAA-14 daylight passes over a region of complex topography (the South Island of New Zealand and adjacent ocean areas) is analyzed, and exploratory cloud-amount and -type climatological distributions are developed. When validated against a set of surface observations, the cloud-amount distributions have no significant bias at seasonal and yearly timescales, and explain between 70% (seasonal) and 90% (annual) of the spatial variance in the surface observations.

The cloud-amount distributions show strong land/sea contrasts. Lowest cloud frequencies are found in the lee of the major alpine feature in the analysis domain (the Southern Alps) and over mountain-sheltered valleys and adjacent sea areas. Over the oceans, cloud frequencies are highest over sub-Antarctic water masses, and range from 90% to 95%. However, over the sea adjacent to the coast on the western side of the Southern Alps, there is a distinct minimum in cloud amount that appears to be related to the orography.

The cloud-type climatological distributions are analyzed in terms of both simple frequency of occurrence and conditional frequency of occurrence, which is the frequency of occurrence as a fraction of the total number of times that the cloud type could have been observed. These distributions reveal the presence of preferred locations for some cloud types. There is strong evidence that uplift over major mountain ranges is a source of transmissive cirrus (enhancing occurrence by a factor of 2) and that the resulting cirrus coverage is most extensive and frequent in spring. Over the ocean areas, SST-related effects may determine the spatial distributions of stratocumulus, with higher frequencies observed over sub-Antarctic waters than over subtropical waters. Also, there is a positive correlation between mean cloud-top height and SST, but no similar relationship is found for other cloud types.

Abstract

Bayesian methods are used to develop a cloud mask classification algorithm for use in an operational sea surface temperature (SST) retrieval processing system for Advanced Very High Resolution Radiometer (AVHRR) local area coverage (LAC) resolution data. Both radiative and spatial features are incorporated in the resulting discriminant functions, which are determined from a large training sample of cloudy and clear observations. This approach obviates the need to specify the arbitrary thresholds used by hierarchical cloud-clearing methods, provides an estimate of the probability that an instantaneous field of view is cloudy (clear), and allows the skill of different cloud discriminant models to be objectively analyzed.

Results show that spatial information is of particular importance in reducing the false alarm rate of the cloudy class. However, while the use of complex textural measures such as gray-level difference statistics—as opposed to simple statistics such as the standard deviation—improves the skill of nighttime cloud-masking algorithms, they are of little advantage during daytime hours.

Cloud mask discriminant models having similar high Kuipers’ performance index scores (i.e., 0.935) are developed for both day and night satellite data from the Southern Hemisphere midlatitudes. Applied to LAC orbital (i.e., operational) data, the characteristics of the cloud masks appear to be similar to those derived from analysis of the training sample data. However, in this case, to enhance processing performance, a hybrid algorithm is employed—obviously cloudy instantaneous fields of view (IFOVs) are first removed via a gross threshold check and the Bayesian method applied only to the remaining IFOVs. This same (hybrid) algorithm is also applied to an ensemble of 30 days of AVHRR LAC data from the New Zealand region. Analysis of the resulting time-composited SST data (means and standard deviations) shows there is little evidence of a day–night bias in the performance of the Bayesian cloud-masking algorithm and that the resulting SST data may be used to determine the variability of oceanographic features.

Although this paper uses AVHRR data to demonstrate the principles of the Bayesian cloud-masking algorithm, there is no reason why the approach could not be used with other instruments.

Abstract

Wind data at time scales from 10 min to 1 h are an important input for modeling the performance of wind farms and their impact on many countries’ national electricity systems. Planners need long-term realistic (i.e., meteorologically spatially and temporally consistent) wind-farm data for projects studying how best to integrate wind power into the national electricity grid. In New Zealand, wind data recorded at wind farms are confidential for commercial reasons, however, and publicly available wind data records are for sites that are often not representative of or are distant from wind farms. In general, too, the public sites are at much lower terrain elevations than hilltop wind farms and have anemometers located at 10 m above the ground, which is much lower than turbine hub height. In addition, when available, the mast records from wind-farm sites are only for a short period. In this paper, the authors describe a novel and practical method to create a multiyear 10-min synthetic wind speed time series for 15 wind-farm sites throughout the country for the New Zealand Electricity Commission. The Electricity Commission (known as the Electricity Authority since 1 October 2010) is the agency that has regulatory oversight of the electricity industry and that provides advice to central government. The dataset was constructed in such a way as to preserve meteorological realism both spatially and temporally and also to respect the commercial secrecy of the wind data provided by power-generation companies.