Search Results

You are looking at 1 - 10 of 15 items for

  • Author or Editor: C. Kidd x
  • Refine by Access: All Content x
Clear All Modify Search
C. Kidd
,
D. Kniveton
, and
E. C. Barrett

Abstract

This paper reviews the basis of passive microwave algorithms that derive rainfall rates directly from relationships between brightness temperatures and rainfall rates established by statistical relationships and empirical calibration. The performance of these algorithms and their present and future roles are assessed in comparison with the increasing number of modeling techniques used for passive microwave rainfall retrievals.

Full access
Danielle C. Verdon-Kidd
and
Anthony S. Kiem

Abstract

Water management in Australia has traditionally been carried out on the assumption that the historical record of rainfall, evaporation, streamflow, and recharge is representative of current and future climatic conditions. However, in many circumstances, this does not adequately address the potential risks to supply security for towns, industry, irrigators, and the environment. This is because the Australian climate varies markedly due to natural cycles that operate over periods of several years to several decades. There is also serious concern about how anthropogenic climate change may exacerbate drought risk in the future. In this paper, the frequency and severity of droughts are analyzed during a range of “climate states” (e.g., different phases of the Pacific, Indian, and/or Southern Oceans) to demonstrate that drought risk varies markedly over interannual through to multidecadal time scales. Importantly, by accounting for climate variability and change on multitemporal scales (e.g., interdecadal, multidecadal, and the palaeo scale), it is demonstrated that the risk of failure of current drought management practices may be better assessed and more robust climate adaptation responses developed.

Full access
Capt. Kenneth P. Kidd, A.C.
and
Capt. Charles K. Reed, A.C.
Full access
Capt. Kenneth P. Kidd, A.C.
and
Capt. Charles K. Reed, A. C.
Full access
Martin C. Todd
,
Chris Kidd
,
Dominic Kniveton
, and
Tim J. Bellerby

Abstract

There are numerous applications in climatology and hydrology where accurate information at scales smaller than the existing monthly/2.5° products would be invaluable. Here, a new microwave/infrared rainfall algorithm is introduced that combines satellite passive microwave (PMW) and infrared (IR) data to account for limitations in both data types. Rainfall estimates are produced at the high spatial resolution and temporal frequency of the IR data using rainfall information from the PMW data. An IRTb–rain rate relationship, variable in space and time, is derived from coincident observations of IRTb and PMW rain rate (accumulated over a calibration domain) using the probability matching method. The IRTb–rain rate relationship is then applied to IR imagery at full temporal resolution.

MIRA estimates of rainfall are evaluated over a range of spatial and temporal scales. Over the global Tropics and subtropics, optimum IR thresholds and IRTb–rain rate relationships are highly variable, reflecting the complexity of dominant cloud microphysical processes. As a result, MIRA shows sensitivity to these variations, resulting in potentially useful improvements in estimate accuracy at small scales in comparison to the Geostationary Operational Environmental Satellite Precipitation Index (GPI) and the PMW-calibrated Universally Adjusted GPI (UAGPI). Unlike some existing PMW/IR techniques, MIRA can successfully capture variability in rain rates at the smallest possible scales. At larger scales MIRA and UAGPI produce very similar improvements over the GPI. The results demonstrate the potential for a new high-resolution rainfall climatology from 1987 onward, using International Satellite Cloud Climatology Project DX and Special Sensor Microwave Imager data. For real-time regional or quasi-global applications, a temporally “rolling” calibration window is suggested.

Full access
R. Layberry
,
D. R. Kniveton
,
M. C. Todd
,
C. Kidd
, and
T. J. Bellerby

Abstract

This paper describes a new high-resolution multiplatform multisensor satellite rainfall product for southern Africa covering the period 1993–2002. The microwave infrared rainfall algorithm (MIRA) employed to generate the rainfall estimates combines high spatial and temporal resolution Meteosat infrared data with infrequent Special Sensor Microwave Imager (SSM/I) overpasses. A transfer function relating Meteosat thermal infrared cloud brightness temperatures to SSM/I rainfall estimates is derived using collocated data from the two instruments and then applied to the full coverage of the Meteosat data. An extensive continental-scale validation against synoptic station data of both the daily MIRA precipitation product and a normalized geostationary IR-only Geostationary Operational Environmental Satellite (GOES) precipitation index (GPI) demonstrates a consistent advantage using the former over the latter for rain delineation. Potential uses for the resulting high-resolution daily rainfall dataset are discussed.

Full access
Michelle Ho
,
Danielle C. Verdon-Kidd
,
Anthony S. Kiem
, and
Russell N. Drysdale

Abstract

Recent advances in the collection and analysis of paleoclimate data have provided significant insights into preinstrumental environmental events and processes, enabling a greater understanding of long-term environmental change and associated hydroclimatic risks. Unfortunately, it is often the case that there is a dearth of readily available paleoclimate data from regions where such insights and long-term data are most needed. The Murray–Darling basin (MDB), known as Australia’s “food bowl,” is an example of such a region where currently there are very limited in situ paleoclimate data available. While previous studies have utilized paleoclimate proxy records of large-scale climate mechanisms to infer preinstrumental MDB hydroclimatic variability, there is a lack of studies that utilize Australian terrestrial proxy records to garner similar information. Given the immediate need for improved understanding of MDB hydroclimatic variability, this paper identifies key locations in Australia where existing and as yet unrealized paleoclimate records will be most useful in reconstructing such information. To identify these key locations, rainfall relationships between MDB and non-MDB locations were explored through correlations and principal component analysis. An objective analysis using optimal interpolation was then used to pinpoint the most strategic locations to further develop proxy records and gain insights into the benefits of obtaining this additional information. The findings reveal that there is potential for the future assembly of high-resolution paleoclimate records in Australia capable of informing MDB rainfall variability, in particular southeast Australia and central-northern Australia. This study highlights the need for further investment in the development of these potential proxy sources to subsequently enable improved assessments of long-term hydroclimatic risks.

Full access
Chris Kidd
,
Dominic R. Kniveton
,
Martin C. Todd
, and
Tim J. Bellerby

Abstract

The development of a combined infrared and passive microwave satellite rainfall estimation technique is outlined. Infrared data from geostationary satellites are combined with polar-orbiting passive microwave estimates to provide 30-min rainfall estimates. Collocated infrared and passive microwave values are used to generate a database, which is accessed by a cumulative histogram matching approach to generate an infrared temperature–rain-rate relationship. The technique produces initial estimates at 30-min and 12-km resolution ready to be aggregated to the user requirements. A 4-month case study over Africa has been chosen to compare the results from this technique with those of some existing rainfall techniques. The results indicate that the technique outlined here has statistical scores that are similar to other infrared/passive microwave combined algorithms. Comparison with the Geostationary Operational Environmental Satellite (GOES) precipitation index shows that while these algorithms result in lower correlation scores, areal statistics are significantly better than either the infrared or passive microwave techniques alone.

Full access
C. Kidd
,
P. Bauer
,
J. Turk
,
G. J. Huffman
,
R. Joyce
,
K.-L. Hsu
, and
D. Braithwaite

Abstract

Satellite-derived high-resolution precipitation products (HRPP) have been developed to address the needs of the user community and are now available with 0.25° × 0.25° (or less) subdaily resolutions. This paper evaluates a number of commonly available satellite-derived HRPPs covering northwest Europe over a 6-yr period. Precipitation products include the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA), the Climate Prediction Center (CPC) morphing (CMORPH) technique, the CPC merged microwave technique, the Naval Research Laboratory (NRL) blended technique, and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) technique. In addition, the Geosynchronous Operational Environmental Satellite (GOES) precipitation index (GPI) and the European Centre for Medium-Range Weather Forecasting (ECMWF) operational forecast model products are included for comparison. Surface reference data from the European radar network is used as ground truth, supported by the Global Precipitation Climatology Centre (GPCC) precipitation gauge analysis and gauge data over the United Kingdom. Measures of correlation, bias ratio, probability of detection, and false alarm ratio are used to evaluate the products. Results show that satellite products generally exhibit a seasonal cycle in correlation, bias ratio, probability of detection, and false alarm ratio, with poorer statistics during the winter. The ECMWF model also shows a seasonal cycle in the correlation, although the results are poorer during the summer, while the bias ratio, probability of detection, and false alarm ratio are consistent through all seasons. Importantly, all the satellite HRPPs underestimate precipitation over northwest Europe in all seasons.

Full access
E. A. Smith
,
J. E. Lamm
,
R. Adler
,
J. Alishouse
,
K. Aonashi
,
E. Barrett
,
P. Bauer
,
W. Berg
,
A. Chang
,
R. Ferraro
,
J. Ferriday
,
S. Goodman
,
N. Grody
,
C. Kidd
,
D. Kniveton
,
C. Kummerow
,
G. Liu
,
F. Marzano
,
A. Mugnai
,
W. Olson
,
G. Petty
,
A. Shibata
,
R. Spencer
,
F. Wentz
,
T. Wilheit
, and
E. Zipser

Abstract

The second WetNet Precipitation Intercomparison Project (PIP-2) evaluates the performance of 20 satellite precipitation retrieval algorithms, implemented for application with Special Sensor Microwave/Imager (SSM/I) passive microwave (PMW) measurements and run for a set of rainfall case studies at full resolution–instantaneous space–timescales. The cases are drawn from over the globe during all seasons, for a period of 7 yr, over a 60°N–17°S latitude range. Ground-based data were used for the intercomparisons, principally based on radar measurements but also including rain gauge measurements. The goals of PIP-2 are 1) to improve performance and accuracy of different SSM/I algorithms at full resolution–instantaneous scales by seeking a better understanding of the relationship between microphysical signatures in the PMW measurements and physical laws employed in the algorithms; 2) to evaluate the pros and cons of individual algorithms and their subsystems in order to seek optimal “front-end” combined algorithms; and 3) to demonstrate that PMW algorithms generate acceptable instantaneous rain estimates.

It is found that the bias uncertainty of many current PMW algorithms is on the order of ±30%. This level is below that of the radar and rain gauge data specially collected for the study, so that it is not possible to objectively select a best algorithm based on the ground data validation approach. By decomposing the intercomparisons into effects due to rain detection (screening) and effects due to brightness temperature–rain rate conversion, differences among the algorithms are partitioned by rain area and rain intensity. For ocean, the screening differences mainly affect the light rain rates, which do not contribute significantly to area-averaged rain rates. The major sources of differences in mean rain rates between individual algorithms stem from differences in how intense rain rates are calculated and the maximum rain rate allowed by a given algorithm. The general method of solution is not necessarily the determining factor in creating systematic rain-rate differences among groups of algorithms, as we find that the severity of the screen is the dominant factor in producing systematic group differences among land algorithms, while the input channel selection is the dominant factor in producing systematic group differences among ocean algorithms. The significance of these issues are examined through what is called “fan map” analysis.

The paper concludes with a discussion on the role of intercomparison projects in seeking improvements to algorithms, and a suggestion on why moving beyond the “ground truth” validation approach by use of a calibration-quality forward model would be a step forward in seeking objective evaluation of individual algorithm performance and optimal algorithm design.

Full access