Joan M. Castro

Empirical Probability Models to Predict Precipitation Levels over Puerto Rico Stations

Abstract A new algorithm is proposed to predict the level of rainfall (above normal, normal, and below normal) in Puerto Rico that relies on probability and empirical models. The algorithm includes a theoretical probability model in which parameters are expressed as regression equations containing observed meteorological variables. Six rainfall stations were used in this study to implement and assess the reliability of the models. The stations, located throughout Puerto Rico, have monthly records that extend back 101 yr. The maximum likelihood method is used to estimate the parameters of the empirical probability models. A variable selection (VS) algorithm identifies the minimum number of variables that maximize the correlation between predictors and a predictand. The VS algorithm is used to identify the initial point and the maximum likelihood is optimized by using the sequential quadratic programming algorithm. Ten years of cross validation were applied to the results from six stations. The proposed met...

Analysis of the Heat Index in the Mesoamerica and Caribbean Region

AbstractHourly data collected from ground stations were used to study the maximum daytime heat index Hi in the Mesoamerica and Caribbean Sea (MAC) region for a 35-yr period (1980–2014). Observations of Hi revealed larger values during the rainy season and smaller values during the dry season. The Hi climatology exhibits the largest values in Mesoamerica, followed by the Greater Antilles and then by the Lesser Antilles. The trend in Hi indicates a notable increasing pattern of 0.05°C yr−1 (0.10°F yr−1), and the trends are more prominent in Mesoamerica than in Caribbean countries. This work also includes the analysis of heat index extreme events (HIEE). Usually the extreme values of the heat index are used for advising heat warning events, and it was found that 45 HIEEs occurred during the studied period. The average duration of HIEE was 2.4 days, and the average relative intensity (excess over the threshold) was 2.4°C (4.3°F). It was found that 82% of HIEE lasted 2 or 2.5 days and 80% exhibited relative in...

Detecting of a Global and Caribbean Climate Change

Weather is defined as what is happening to the atmosphere at any given time while climate is what would be expected to occur at any given time of the year based on many years of meteorological observations. Change in climate constitutes shifts in meteorological conditions lasting a few years or longer. The climate change can occur in a single meteorological variable or in a group of variables affecting a region or the entire Earth (Burroughs, 2001). It is expected that a climate change can be expressed by the behaviour of time series of meteorological variables and in this study, a meteorological variable that expresses a climate change is called a climate indicator. Over the years, the climate of the Earth has changed due to natural or anthropogenic factors, and the research community is concentrating on the identification of the evidences of these changes. However, there are some uncertainties about the occurrence of a significant climate change and especially the time when the changes have become evident. The main purpose of this chapter is to introduce a statistical test to determine when a significant climate change has occurred assuming that a climate indicator is available. A climate indicator is a meteorological variable that reveals the climate of a region or a given part of the Earth. The suggested statistical test will be applied to detect climate changes at the global and Caribbean scale using several climatic indicators. During the last 140 years the Earth has been experimented several climate changes, which have been documented by several researchers (Huntingford et al., 2006; Hansen 2005; Easterling et al., 2000; Battisti el al., 1997; She and Krueger 2004; and Barnett et al., 1999). For example, Easterling et al., (1997) reported that the global mean surface air temperature has risen about 0.5° C during the 20th century. A large part of the world ocean has shown coherent changes of heat content during the last 50 years (Leuliette et al., 2004). Frich et al., (2002) claim that during the second half of the 20th century the world has become both warmer and wetter for global land areas and currently wet periods produce significantly larger rainfall than a few decades ago. These observed extreme events are in line with the expected changes due to the new greenhouse conditions. Global warming is affecting human lives, and in particular is severely impacting the agriculture forestry and in general the economy (Salinger 2005). For instance, Easterling et al., (2000) pointed out that in the United States since 1987 more than 360 weather events have produced losses in excess of

An algorithm to improve the NEXRAD rain rate estimates

5 million each event with several catastrophic consequences. The temperature in globe has increased during the last 140 years, because that the number of heat waves has increased (Schar et al., 2004; Changnon et al., 2000). Global warming is a real process that is leading to

Rainfall estimation from convective storms using the hydro-estimator and NEXRAD

A Doppler WSR-88D radar (NEXRAD) is currently operating in Cayey (East of PR, at 886 m). Reflectivity and rain rate (Z-R) relationships were developed using a dense network of rain gauges to validate radar estimates. These equations intent to correct the radar estimates for seasonal effects. Equations were derived for wet and dry seasons over Puerto Rico. To minimize the errors due to beam block and clutter, equations were derived with 20 rain gauges located 15 km or less from the radar location.