Background In Australia, grassland curing (senescence) is an essential component in fire danger calculations. In seven (out of eight) states/territories in Australia, operational curing data are derived from the MapVictoria satellite model. From 2013 to 2023, MapVictoria data have been calculated using MODerate resolution Imaging Spectroradiometer (MODIS) data from the Terra satellite. Terra has exceeded its designed mission lifetime, but the continuation of satellite curing data is crucial for fire agencies to continue fire danger calculations. Aims The aim of this study was to adjust the MapVictoria model so it could be calculated using a newer satellite sensor system: Visible Infrared Imaging Radiometer Suite (VIIRS). Methods Data from the VIIRS bands were adjusted to match those of MODIS using timeseries from 2013 to 2020. The adjusted VIIRS bands were used to derive a VIIRS curing model: ‘viirs-mvcuring’. Key results The viirs–mvcuring model exhibited lower curing estimates than MODIS by up to 2.6% in Northern sites and 1.4% in Southern sites and exhibited lower curing estimates than ground-based curing by 0.1% in Northern sites and 3.5% in Southern sites. Conclusions The development of the viirs–mvcuring model has ensured continued availability of satellite curing data. Implications The transition to VIIRS will provide continued input of curing into fire danger calculations across Australia.

The Bureau of Meteorology’s 31st Annual Research and Development workshop was held in Melbourne, in November 2019 and had the theme ‘Forecasting for the Future: New science for improved weather, water, ocean and climate services’. Environmental forecast services range from routine daily weather forecasts for the public, to seasonal outlooks and climate projections aimed at informing decisions by agriculture and water managers. Emergency managers rely on highly customised services during times of extreme weather such as fires, floods, heatwaves and tropical cyclones. Industry specific advice on the influences of climate variability and change enables society to deal with the challenges posed by our unique climate, both today and in the future. In order to meet the increasing demands of customers and deliver greater impact and value, service providers are transforming the way in which they work. This transformation process requires significantly enhanced capability in science and technology. Key advances that enhance our abilities to forecast from hourly to decadal scales include:

The appropriate design of infrastructure in tropical cyclone (TC) prone regions requires an understanding of the hazard risk profile underpinned by an accurate, homogenous long-term TC dataset. The existing Australian region TC archive, or ‘best track’ (BT), suffers from inhomogeneities and an incomplete long-term record of key TC parameters. This study assesses mostly satellite-based objective techniques for 1981–2016, the period of a geostationary satellite imagery dataset corrected for navigation and calibration issues. The satellite-based estimates of Australian-region TCs suffer from a general degradation in the 1981–1988 period owing to lower quality and availability of satellite imagery.The quality of the objective techniques for both intensity and structure is compared to the reference BT 2003–2016 estimates. For intensity the Advanced Dvorak Technique algorithm corresponds well with the BT 2003–2016, when the algorithm can use passive microwave data (PMW) as an input. For the period prior to 2003 when PMW data is unavailable, the intensity algorithm has a low bias. Systematic corrections were made to the non-PMW objective estimates to produce an extended (1989–2016) homogeneous dataset of maximum wind that has sufficient accuracy to be considered for use where a larger homogeneous sample size is valued over a shorter more accurate period of record. An associated record of central pressure using the Courtney-Knaff-Zehr wind pressure relationship was created.For size estimates, three techniques were investigated: the Deviation Angle Variance and the ‘Knaff’ techniques (IR-based), while the ‘Lok’ technique used model information (ECMWF reanalysis dataset and TC vortex specification from ACCESS-TC). However, results lacked sufficient skill to enable extension of the reliable period of record. The availability of scatterometer data makes the BT 2003–2016 dataset the most reliable and accurate. Recommendations regarding the best data source for each parameter for different periods of the record are summarised.

Cloud-top height (CTH) and cloud-top temperature (CTT) retrieved from the Himawari-8 observations are evaluated using the active shipborne radar-lidar observations derived from the 31-day Clouds, Aerosols, Precipitation Radiation and Atmospheric Composition over the Southern Ocean (CAPRICORN) experiment in 2016 and 1-yr observations from the spaceborne Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) cloud product over a large sector of the Southern Ocean. The results show that the Himawari-8 CTH (CTT) retrievals agree reasonably well with both the shipborne estimates, with a correlation coefficient of 0.837 (0.820), a mean bias error of 0.226 km (-2.526 degrees C), and an RMSE of 1.684 km (10.069 degrees C). In the comparison with CALIOP, the corresponding quantities are found to be 0.786 (0.480), -0.570 km (1.343 degrees C), and 2.297 km (25.176 degrees C). The Himawari-8 CTH (CTT) generally falls between the physical CTHs observed by CALIOP and the shipborne radar-lidar estimates. However, major systematic biases are also identified. These errors include (i) a low (warm) bias in CTH (CTT) for warm liquid cloud type, (ii) a cold bias in CTT for supercooled liquid water cloud type, (iii) a lack of CTH at 3 km that does not have a corresponding gap in CTT, (iv) a tendency of misclassifying some low-/mid-top clouds as cirrus and overlap cloud types, and (v) a saturation of CTH (CTT) around 10 km (-40 degrees C), particularly for cirrus and overlap cloud types. Various challenges that underpin these biases are also explored, including the potential of parallax bias, low-level inversion, and cloud heterogeneity.

Observations of top-of-atmosphere radiances from the Advanced Himawari Imager (AHI) blue, green, and red spectral bands can be used to produce high-temporal-resolution, true-color imagery at 1-km spatial resolution over the Asia–Pacific region. To enhance interpretability and aesthetic appearance of these images, the top-of-atmosphere radiance data are processed to remove the Rayleigh-scattered atmospheric component, corrected for limb effects, blended with brightness temperature data from a thermal infrared window band at night, and the resultant imagery adjusted to optimize contrast. The contribution of Rayleigh scattering to the AHI observations is calculated by interpolating radiative transfer parameters from a preconstructed set of lookup tables, which are specifically created for the Himawari-8 AHI instrument. A surface reflectance value for each pixel is calculated after the Rayleigh contribution is removed. The spectrally dependent reflectance values produced from the lookup table differ from the exact calculation by up to 18% at the planetary limb, over 100% at the solar terminator, and by less than 0.5% at low to moderate solar and sensor zenith angles. The subsequent corrections applied for limb effects mitigate the areas with high interpolation error, which slightly reduces the spatial coverage, but provides Rayleigh-corrected surface reflectance products that have interpolation errors at or below 0.5%. Resolution sharpening increases the nominal pixel size from 1000 to 500 m while still producing sharp images. The resultant images are colorful, visually intuitive, high contrast, and of sufficient spatial and temporal resolution to provide a unique and complementary observational tool for use by weather forecasters and the general public alike.

Four sea surface temperature (SST) diurnal variation (DV) models have been compared against Multifunctional Transport Satellite-1R (MTSAT-1R) SST measurements over the Tropical Warm Pool (TWP) region (90 degrees E-170 degrees E, 25 degrees S-15 degrees N) for 4 months from January to April 2010. The four models include one empirical model formulated by Chelle Gentemann (hereafter CG03), one physical model proposed by Zeng and Beljaars in 2005 (ZB05) and its updated version (ZB+T), and one air-sea coupled model (the Met Office Unified Model Global Coupled configuration 2, GC2) with ZB05 warm layer scheme added on top of the standard configuration. The sensitivity of the v3 MTSAT-1R data to the "true'' changes in SST is first investigated using drifting buoys and is estimated to be 0.60 +/- 0.05. This being significantly different from 1, the models are validated against MTSAT-1R data and the same data scaled by the inverse of the sensitivity (representing an estimate of the true variability). Results indicate that all models are able to capture the general DV patterns but with differing accuracies and features. Specifically, CG03 and ZB+T underestimate strong (> 2 K) DV events' amplitudes especially if we assume that sensitivity-scaled MTSAT-1R variability is most realistic. ZB05 can effectively capture the DV cycles under most DV and wind conditions, as well as the DV spatial distribution. GC2 tends to overestimate small-moderate (< 2 K) DV events but can reasonably predict large DV events. One to three hour lags in warming start and peak times are found in GC2.

The validation of convective processes in global climate models (GCMs) could benefit from the use of large datasets that provide long-term climatologies of the spatial statistics of convection. To that regard, echo top heights (ETHs), convective areas, and frequencies of mesoscale convective systems (MCSs) from 17 years of data from a C-band polarization (CPOL) radar are analyzed in varying phases of the Madden–Julian Oscillation (MJO) and northern Australian monsoon in order to provide ample validation statistics for GCM validation. The ETHs calculated using velocity texture and reflectivity provide similar results, showing that the ETHs are insensitive to various techniques that can be used. Retrieved ETHs are correlated with those from cloud top heights retrieved by Multifunctional Transport Satellites (MTSATs), showing that the ETHs capture the relative variability in cloud top heights over seasonal scales. Bimodal distributions of ETH, likely attributable to the cumulus congestus clouds and mature stages of convection, are more commonly observed when the active phase of the MJO is over Australia due to greater mid-level moisture during the active phase of the MJO. The presence of a convectively stable layer at around 5 km altitude over Darwin inhibiting convection past this level can explain the position of the modes at around 2–4 km and 7–9 km. Larger cells were observed during break conditions compared to monsoon conditions, but only during the inactive phase of the MJO. The spatial distributions show that Hector, a deep convective system that occurs almost daily during the wet season over the Tiwi Islands, and sea-breeze convergence lines are likely more common in break conditions. Oceanic MCSs are more common during the night over Darwin. Convective areas were generally smaller and MCSs more frequent during active monsoon conditions. In general, the MJO is a greater control on the ETHs in the deep convective mode observed over Darwin, with higher distributions of ETH when the MJO is active over Darwin.

Abstract. The validation of convective processes in general circulation models requires the use of large datasets that provide long term climatologies of the spatial statistics of convection. To that regard, echo top heights (ETHs) retrieved from 17 years of data from C-band POLarization (CPOL) Radar are analyzed in varying phases of the Madden-Julian Oscillation (MJO) and Northern Australian Monsoon in order to provide ample validation statistics for the Department of Energy's next generation Earth Energy Exascale Model. In this paper, ETHs are retrieved using a novel methodology that uses the texture of radial velocity. Comparisons of retrieved ETHs against satellite retrieved cloud top heights from the split window technique show that the estimated ETH are correlated with, and, on average, are within 3 km of satellite retrieved cloud top heights. Using this technique gives comparable ETHs compared to using a reflectivity threshold. Bimodal distributions of ETH, likely attributable to the cumulus congestus and mature stages of convection, are more commonly observed when the active phase of the MJO is away from Australia. The presence of a convectively stable layer at around 5 km altitude over Darwin inhibiting convection past this level can explain the position of the modes at around 5 to 6 km and 12 to 13 km respectively. The spatial distributions show that Hector, a deep convective system that occurs almost daily during the wet season over the Tiwi Islands, and seabreeze convergence lines are likely more common in break conditions. Oceanic mesoscale convective systems (MCSes) are likely more common during the night. Unimodal distributions of ETH are more common during monsoon conditions and during an active MJO over Darwin, consistent with the presence of widespread MCSes that are commonly associated with both the MJO and the Northern Australian Monsoon. In general, the MJO is a greater control of the ETHs observed over Darwin, with generally both lower and more unimodal distributions of ETH when the MJO is active over Darwin.

Diurnal variation (DV) of sea surface temperature (SST) plays an important role in air–sea interaction. We have validated four months (January to April 2010) of the version 3 Australian Bureau of Meteorology reprocessed Multifunction Transport SATellite-1R (v3 MTSAT-1R) SST data over the Tropical Warm Pool (TWP) region (90°E to 170°E, 25°S to 15°N) against both drifting buoy and Advanced Along-Track Scanning Radiometer (AATSR) SST data. Validation against collocated point measurements from drifting buoys, under conditions where the surface ocean is well-mixed, shows that overall the v3 MTSAT-1R SSTs perform well with an average bias of 0.00°C and a 0.73°C standard deviation (STD). The average daytime and night-time mean bias is −0.06°C and 0.08°C, respectively. For all hours of the diurnal cycle, the mean biases are within ±0.25°C, indicating the consistency between day and night v3 MTSAT-1R data. However, on average, the v3 MTSAT-1R SSTs are overestimated at cold SSTs and underestimated at warm SSTs. Similar results are obtained from validation against the AATSR satellite SSTs but with smaller STD (0.48°C) and smaller average daytime and night-time mean biases (−0.04°C and 0.06°C, respectively). These results indicate that the v3 MTSAT-1R data set is suitable for SST DV investigations and validation of DV models. Using the validated v3 MSTAT-1R data, together with surface wind speed and solar shortwave insolation (SSI) outputs from the Australian Community Climate and Earth-System Simulator – Regional (ACCESS-R) numerical prediction model, we investigate SST DV events over the TWP region. Good correlation is found between DV events and low wind and high SSI conditions. The dominant role of wind speed in SST DV events over the SSI is also revealed.

The Bureau of Meteorology (the Bureau) has an extensive archive of geostationary satellite imagery, which is derived from a number of data sources. Most of this data now exists in a single data format, namely Man computer Interactive Data Access System (McIDAS) AREA. The McIDAS system was commissioned in the Bureau in 1985 and used to digitise locally received analogue GMS-3 data as its geostationary data source. This was the first McIDAS system to handle real time GMS data. McIDAS is the principal display and analysis tool utilised by the Bureau and is currently used to analyse tropical cyclone (TC) data received from satellites.This study is concentrated on data calibration from Geostationary Meteorological Satellite (GMS) 1 to 3 for the years 1981 to 1989 as these data had recently been obtained from the Japan Meteorological Agency (JMA) in VISSR format. It is hoped that the new data can be utilised within the McIDAS system to perform reanalysis of TCs to give a consistent 30 year record.This study revealed many possible sources of uncertainty with the initial calibration of the GMS data and highlighted issues with the methods of utilisation within McIDAS. It was discovered that there is the possibility, during the solar equinox periods, that error in temperature estimates from GMS data for temperatures of 160 K and above could exceed +/- 6 K due to the calibration procedure. Smaller errors due to the implementation of the calibration procedure are expected at other times during the year.A comparison of the JMA GMS data calibration with the International Satellite Cloud Climatology Project (ISCCP) and HURicane SATellite (HURSAT) calibrations found a definite trend toward colder temperatures being produced when using the ISCCP calibration but no noticeable temperature trend when using the HURSAT calibration.HURSAT data were then selected to investigate how data calibration affected Dvorak analysis and the determination of TC intensity. Dvorak analysis for 11 cyclones using the JMA calibration and the HURSAT calibration was undertaken. This found that there was no detectable difference unless an eye or embedded centre pattern was noticed in the scene. Only 39 such scenes were identified from the 703 scenes examined. Of these 39 scenes three showed a difference in analysis of 1.0 T number, eight showed +/- 0.5 T number difference and 28 were ranked as exactly the same. This indicates that Dvorak analysis will not be affected by small errors in calibration.Presently, the Bureau has more than 30 years of data of Geostationary Satellite IR radiance and temperature data in McIDAS AREA format. It is now possible to use a single system (McIDAS) and a single method (EIR Dvorak) to reanalyse all available geostationary data for the Australian TC region.

We investigate the performance of an eddy resolving regional ocean forecasting system of the East Australian Current (EAC) for both ensemble optimal interpolation (EnOI) and ensemble Kalman filter (EnKF) with a focus on open boundary model nesting solutions. The performance of nesting into a global re-analysis; nesting into the system's own analysis; and nesting into a free model is quantified in terms of forecast innovation error. Nesting in the global reanalysis is found to yield the best results. This is closely followed by the system that nests inside its own analysis, which seems to represent a viable practical option in the absence of a suitable analysis to nest within. Nesting into a global reanalysis without data assimilation and nesting into an unconstrained model were both found to be unable to constrain the mesoscale circulation at all times. We also find that for a specific interior area of the domain where the EAC separation takes place, there is a mixture of results for all the systems investigated here and that, whilst the application of EnKF generates the best results overall, there are still times when not even this method is able to constrain the circulation in this region with the available observations. (C) 2014 Elsevier Ltd. All rights reserved.

The Australian Bureau of Meteorology (the Bureau) is producing 1 km resolution sea surface temperature (SST) products in real time from data from the Advanced Very High Resolution Radiometer (AVHRR) sensors on board NOAA polar orbiter platforms received at the Bureau’s satellite reception facilities. As part of the Australian Integrated Marine Observing System (IMOS: http://www.imos.org.au) the Bureau has recently upgraded its SST processing system to comply with the GHRSST (Group for High Resolution SST) Data Processing Specification v2.0 (Casey et al., 2010a) and up-to-date processing that meets or exceeds worlds best practice. The significant components include the use of regional rather than global buoy SSTs for satellite SST calibration, noise resistant methods of SST coefficient estimation, the development of a match-up database (MDB), calculation of single sensor error statistics (SSES), an improvement in cloud identification, and the generation and distribution of SST products in GHRSST L2P and L3C formats.

The marine environment in the shallow waters to the north of Australia is poorly characterised. Vast areas have never been surveyed and relatively little is known about the composition of the water mass, the properties of the seabed and the bathymetry of the shallowest areas. Satellite-based optical remote sensing has potential to provide wide-ranging, inexpensive and frequent coverage of the region, but techniques for the estimation of environmental parameters from satellite data are dependent on robust inversion algorithms and a high level of knowledge about the prevalent environmental conditions. In November 2005 a suite of physical, optical and biological measurements were collected in the Torres Strait (near Thursday Island, Australia) and in the Gulf of Carpentaria. Above-water reflectance measurements were made along the cruise track using a 3-channel continuous sampling spectroradiometer. A semianalytic model was applied to the reflectance measurements to estimate water column depth and in-water optical properties. Model retrieved backscattering compared favourably, both in terms of spectral slope and magnitude, with in-water measurements. The model retrieved phytoplankton absorption compared favourably with laboratory measurements where these were available. Model retrieved depth values were within 10% of sounded depths at 10 of 11 measurement sites where the bottom contribution to the measured above water reflectance exceeded 15%.