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  • Author or Editor: Thomas W. Giambelluca x
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Matthew P. Lucas
,
Ryan J. Longman
,
Thomas W. Giambelluca
,
Abby G. Frazier
,
Jared Mclean
,
Sean B. Cleveland
,
Yu-Fen Huang
, and
Jonghyun Lee

Abstract

Gridded monthly rainfall estimates can be used for a number of research applications, including hydrologic modeling and weather forecasting. Automated interpolation algorithms, such as the “autoKrige” function in R, can produce gridded rainfall estimates that validate well but produce unrealistic spatial patterns. In this work, an optimized geostatistical kriging approach is used to interpolate relative rainfall anomalies, which are then combined with long-term means to develop the gridded estimates. The optimization consists of the following: 1) determining the most appropriate offset (constant) to use when log-transforming data; 2) eliminating poor quality data prior to interpolation; 3) detecting erroneous maps using a machine learning algorithm; and 4) selecting the most appropriate parameterization scheme for fitting the model used in the interpolation. Results of this effort include a 30-yr (1990–2019), high-resolution (250-m) gridded monthly rainfall time series for the state of Hawai‘i. Leave-one-out cross validation (LOOCV) is performed using an extensive network of 622 observation stations. LOOCV results are in good agreement with observations (R 2 = 0.78; MAE = 55 mm month−1; 1.4%); however, predictions can underestimate high rainfall observations (bias = 34 mm month−1; −1%) due to a well-known smoothing effect that occurs with kriging. This research highlights the fact that validation statistics should not be the sole source of error assessment and that default parameterizations for automated interpolation may need to be modified to produce realistic gridded rainfall surfaces. Data products can be accessed through the Hawai‘i Data Climate Portal (HCDP; http://www.hawaii.edu/climate-data-portal).

Significance Statement

A new method is developed to map rainfall in Hawai‘i using an optimized geostatistical kriging approach. A machine learning technique is used to detect erroneous rainfall maps and several conditions are implemented to select the optimal parameterization scheme for fitting the model used in the kriging interpolation. A key finding is that optimization of the interpolation approach is necessary because maps may validate well but have unrealistic spatial patterns. This approach demonstrates how, with a moderate amount of data, a low-level machine learning algorithm can be trained to evaluate and classify an unrealistic map output.

Full access
Thomas W. Giambelluca
,
Qi Chen
,
Abby G. Frazier
,
Jonathan P. Price
,
Yi-Leng Chen
,
Pao-Shin Chu
,
Jon K. Eischeid
, and
Donna M. Delparte
Full access
Alison D. Nugent
,
Ryan J. Longman
,
Clay Trauernicht
,
Matthew P. Lucas
,
Henry F. Diaz
, and
Thomas W. Giambelluca

Abstract

Hurricane Lane (2018) was an impactful event for the Hawaiian Islands and provided a textbook example of the compounding hazards that can be produced from a single storm. Over a 4-day period, the island of Hawaiʻi received an island-wide average of 424 mm (17 in.) of rainfall, with a 4-day single-station maximum of 1,444 mm (57 in.), making Hurricane Lane the wettest tropical cyclone ever recorded in Hawaiʻi (based on all available quantitative records). Simultaneously, fires on the islands of nearby Maui and Oʻahu burned 1,043 ha (2,577 ac) and 162 ha (400 ac), respectively. Land-use characteristics and antecedent moisture conditions exacerbated fire hazard, and both fire and rain severity were influenced by the storm environment and local topographical features. Broadscale subsidence around the storm periphery and downslope winds resulted in dry and windy conditions conducive to fire, while in a different region of the same storm, preexisting convection, incredibly moist atmospheric conditions, and upslope flow brought intense, long-duration rainfall. The simultaneous occurrence of rain-driven flooding and landslides, high-intensity winds, and multiple fires complicated emergency response. The compounding nature of the hazards produced during the Hurricane Lane event highlights the need to improve anticipation of complex feedback mechanisms among climate- and weather-related phenomena.

Free access
Thomas W. Giambelluca
,
Dirk Hölscher
,
Therezinha X. Bastos
,
Reginaldo R. Frazão
,
Michael A. Nullet
, and
Alan D. Ziegler

Abstract

Regional climatic change, including significant reductions in Amazon Basin evaporation and precipitation, has been predicted by numerical simulations of total tropical forest removal. These results have been shown to be very sensitive to the prescription of the albedo shift associated with conversion from forest to a replacement land cover. Modelers have so far chosen to use an “impoverished grassland” scenario to represent the postforest land surface. This choice maximizes the shifts in land surface parameters, especially albedo (fraction of incident shortwave radiation reflected by the surface). Recent surveys show secondary vegetation to be the dominant land cover for some deforested areas of the Amazon. The characteristics of secondary vegetation as well as agricultural land covers other than pasture have received little attention from field scientists in the region. This paper presents the results of field measurements of radiation flux over various deforested surfaces on a small farm in the eastern Amazonian state of Pará. The albedo of fields in active use was as high as 0.176, slightly less than the 0.180 recently determined for Amazonian pasture and substantially less than the 0.19 commonly used in GCM simulations of deforestation. For 10-yr-old secondary vegetation, albedo was 0.135, practically indistinguishable from the recently published mean primary forest albedo of 0.134. Measurements of surface temperature and net radiation show that, despite similarity in albedo, secondary vegetation differs from primary forest in energy and mass exchange. The elevation of midday surface temperature above air temperature was found to be greatest for actively and recently farmed land, declining with time since abandonment. Net radiation was correspondingly lower for fields in active or recent use. Using land cover analyses of the region surrounding the study area for 1984, 1988, and 1991, the pace of change in regional-mean albedo is estimated to have declined and appears to be leveling at a value less than 0.03 above that of the original forest cover.

Full access
Ryan J. Longman
,
Abby G. Frazier
,
Andrew J. Newman
,
Thomas W. Giambelluca
,
David Schanzenbach
,
Aurora Kagawa-Viviani
,
Heidi Needham
,
Jeffrey R Arnold
, and
Martyn P. Clark

Abstract

Spatially continuous data products are essential for a number of applications including climate and hydrologic modeling, weather prediction, and water resource management. In this work, a distance-weighted interpolation method used to map daily rainfall and temperature in Hawaii is described and assessed. New high-resolution (250 m) maps were developed for daily rainfall and daily maximum (T max) and minimum (T min) near-surface air temperature for the period 1990–2014. Maps were produced using climatologically aided interpolation, in which station anomalies were interpolated using an optimized inverse distance weighting approach and then combined with long-term means to produce daily gridded estimates. Leave-one-out cross validation was performed to assess the quality of the final daily grids. The median absolute prediction error for rainfall was 0.1 mm with an average overprediction (+0.6 mm) on days when total rainfall was less than 1 mm. On days with total rainfall greater than 1 mm, median absolute prediction errors were 2 mm and rainfall was typically underpredicted above the 10-mm threshold. For daily temperature, median absolute prediction errors were 3.1° and 2.8°C for T max and T min, respectively. On average, this method overpredicted T max (+1.1°C) and T min (+1.5°C), and errors varied considerably among stations. Errors for all variables exhibited significant seasonal variations. However, the annual range of errors was small. The methods presented here provide an effective approach for mapping daily weather fields in a topographically diverse region and improve on previous products in their spatial resolution, time period of coverage, and use of data.

Full access
Ryan J. Longman
,
Mathew P. Lucas
,
Jared Mclean
,
Sean B. Cleveland
,
Keri Kodama
,
Abby G. Frazier
,
Katie Kamelamela
,
Aimee Schriber
,
Michael Dodge II
,
Gwen Jacobs
, and
Thomas W. Giambelluca

Abstract

The Hawai‘i Climate Data Portal (HCDP) is designed to facilitate streamlined access to a wide variety of climate data and information for the State of Hawai‘i. Prior to the development of the HCDP, gridded climate products and point datasets were fragmented, outdated, not easily accessible, and not available in near-real-time. To address these limitations, HCDP researchers developed the cyber-infrastructure necessary to 1) operationalize data acquisition and product production in a near-real-time environment, and 2) make data and products easily accessible to a wide range of users. The HCDP hosts several high-resolution (250 m) gridded products including monthly rainfall and daily temperature (maximum, minimum, and mean), station data, and gridded future projections of rainfall and temperature. HCDP users can visualize both gridded and point data, create and download custom maps, and query station and gridded data for export with relative ease. The “virtual station” feature allows users to create a climate time series at any grid point. The primary objective of the HCDP is to promote sharing and access to data and information to streamline research activities, improve awareness, and promote the development of tools and resources that can help to build adaptive capacities. The HCDP products have the potential to serve a wide range of users including researchers, resource managers, city planners, engineers, teachers, students, civil society organizations, and the broader community.

Open access