Abstract

A comprehensive evaluation of split-window and triple-window algorithms to estimate land surface temperature (LST) from Geostationary Operational Environmental Satellites (GOES) that were previously described by Sun and Pinker is presented. The evaluation of the split-window algorithm is done against ground observations and against independently developed algorithms. The triple-window algorithm is evaluated only for nighttime against ground observations and against the Sun and Pinker split-window (SP-SW) algorithm. The ground observations used are from the Atmospheric Radiation Measurement Program (ARM) Central Facility, Southern Great Plains site (April 1997–March 1998); from five Surface Radiation Budget Network (SURFRAD) stations (1996–2000); and from the Oklahoma Mesonet. The independent algorithms used for comparison include the National Oceanic and Atmospheric Administration/National Environmental Satellite, Data and Information Service operational method and the following split-window algorithms: that of Price, that of Prata and Platt, two versions of that of Ulivieri, that of Vidal, two versions of that of Sobrino, that of Coll and others, the generalized split-window algorithm as described by Becker and Li and by Wan and Dozier, and the Becker and Li algorithm with water vapor correction. The evaluation against the ARM and SURFRAD observations indicates that the LST retrievals from the SP-SW algorithm are in closer agreement with the ground observations than are the other algorithms tested. When evaluated against observations from the Oklahoma Mesonet, the triple-window algorithm is found to perform better than the split-window algorithm during nighttime.

Abstract

Weekly average satellite-based estimates of latent heat flux (LHTFL) are used to characterize spatial patterns and temporal variability in the intraseasonal band (periods shorter than 3 months). As expected, the major portion of intraseasonal variability of LHTFL is due to winds, but spatial variability of humidity and SST are also important. The strongest intraseasonal variability of LHTFL is observed at the midlatitudes. It weakens toward the equator, reflecting weak variance of intraseasonal winds at low latitudes. It also decreases at high latitudes, reflecting the effect of decreased SST and the related decrease of time-mean humidity difference between heights z = 10 m and z = 0 m. Within the midlatitude belts the intraseasonal variability of LHTFL is locally stronger (up to 50 W m⁻²) in regions of major SST fronts (like the Gulf Stream and Agulhas). Here it is forced by passing storms and is locally amplified by unstable air over warm SSTs. Although weaker in amplitude (but still significant), intraseasonal variability of LHTFL is observed in the tropical Indian and Pacific Oceans due to wind and humidity perturbations produced by the Madden–Julian oscillations. In this tropical region intraseasonal LHTFL and incoming solar radiation vary out of phase so that evaporation increases just below the convective clusters.

Over much of the interior ocean where the surface heat flux dominates the ocean mixed layer heat budget, intraseasonal SST cools in response to anomalously strong upward intraseasonal LHTFL. This response varies geographically, in part because of geographic variations of mixed layer depth and the resulting variations in thermal inertia. In contrast, in the eastern tropical Pacific and Atlantic cold tongue regions intraseasonal SST and LHTFL are positively correlated. This surprising result occurs because in these equatorial upwelling areas SST is controlled by advection rather than by surface fluxes. Here LHTFL responds to rather than drives SST.

ABSTRACT

Land surface temperature (LST) is an important climate parameter that controls the surface energy budget. For climate applications, information is needed at the global scale with representation of the diurnal cycle. To achieve global coverage there is a need to merge about five independent geostationary (GEO) satellites that have different observing capabilities. An issue of practical importance is the merging of independent satellite observations in areas of overlap. An optimal approach in such areas could eliminate the need for redundant computations by differently viewing satellites. We use a previously developed approach to derive information on LST from GOES-East (GOES-E), modify it for application to GOES-West (GOES-W) and implement it simultaneously across areas of overlap at 5-km spatial resolution. We evaluate the GOES-based LST against in situ observations and an independent MODIS product for the period of 2004–09. The methodology proposed minimizes differences between satellites in areas of overlap. The mean and median values of the differences in monthly mean LST retrieved from GOES-E and GOES-W at 0600 UTC for July are 0.01 and 0.11 K, respectively. Similarly, at 1800 UTC the respective mean and median value of the differences were 0.15 and 1.33 K. These findings can provide guidelines for potential users to decide whether the reported accuracy based on one satellite alone, meets their needs in area of overlap. Since the 6 yr record of LST was produced at hourly time scale, the data are well suited to address scientific issues that require the representation of LST diurnal cycle or the diurnal temperature range (DTR).

Abstract

A large number of water- and climate-related applications, such as drought monitoring, are based on spaceborne-derived relationships between land surface temperature (LST) and the normalized difference vegetation index (NDVI). The majority of these applications rely on the existence of a negative slope between the two variables, as identified in site- and time-specific studies. The current paper investigates the generality of the LST–NDVI relationship over a wide range of moisture and climatic/radiation regimes encountered over the North American continent (up to 60°N) during the summer growing season (April–September). Information on LST and NDVI was obtained from long-term (21 years) datasets acquired with the Advanced Very High Resolution Radiometer (AVHRR). It was found that when water is the limiting factor for vegetation growth (the typical situation for low latitudes of the study area and during the midseason), the LST–NDVI correlation is negative. However, when energy is the limiting factor for vegetation growth (in higher latitudes and elevations, especially at the beginning of the growing season), a positive correlation exists between LST and NDVI. Multiple regression analysis revealed that during the beginning and the end of the growing season, solar radiation is the predominant factor driving the correlation between LST and NDVI, whereas other biophysical variables play a lesser role. Air temperature is the primary factor in midsummer. It is concluded that there is a need to use empirical LST–NDVI relationships with caution and to restrict their application to drought monitoring to areas and periods where negative correlations are observed, namely, to conditions when water—not energy—is the primary factor limiting vegetation growth.

Abstract

The impacts of high-frequency surface forcing in the upper ocean over the equatorial Pacific are investigated using a nonlinear reduced-gravity isopycnal ocean circulation model forced by daily and monthly mean forcing. The simulated sea surface temperature (SST) in the daily forcing experiment is colder than that in the monthly forcing experiment near the equator. A mixed layer heat budget calculation shows that the net surface heat flux is primarily responsible for the SST difference in the western Pacific, while zonal advection accounts for the SST difference in the eastern Pacific where other budget terms are large but canceling each other. The daily forcing primarily enhances vertical mixing that reduces the vertical shear of the upper ocean. It also changes the net heat into the ocean through two contrasting processes: one is the increased surface latent heat loss induced by transient winds and the other is colder SST due to stronger mixing, which further reduces heat loss at the surface. As a result, the annual mean net surface heat flux into the ocean is reduced and the meridional thermal advection is weaker. The daily forcing also impacts the variation of the thermocline through a changing mixed layer depth so that the temperature in the simulation with the daily forcing is warmer around the thermocline.

Polar regions have great sensitivity to climate forcing; however, understanding of the physical processes coupling the atmosphere and ocean in these regions is relatively poor. Improving our knowledge of high-latitude surface fluxes will require close collaboration among meteorologists, oceanographers, ice physicists, and climatologists, and between observationalists and modelers, as well as new combinations of in situ measurements and satellite remote sensing. This article describes the deficiencies in our current state of knowledge about air–sea surface fluxes in high latitudes, the sensitivity of various high-latitude processes to changes in surface fluxes, and the scientific requirements for surface fluxes at high latitudes. We inventory the reasons, both logistical and physical, why existing flux products do not meet these requirements. Capturing an annual cycle in fluxes requires that instruments function through long periods of cold polar darkness, often far from support services, in situations subject to icing and extreme wave conditions. Furthermore, frequent cloud cover at high latitudes restricts the availability of surface and atmospheric data from visible and infrared (IR) wavelength satellite sensors. Recommendations are made for improving high-latitude fluxes, including 1) acquiring more in situ observations, 2) developing improved satellite-flux-observing capabilities, 3) making observations and flux products more accessible, and 4) encouraging flux intercomparisons.