Surface wind convergence as a short-term predictor of cloud-to-ground lightning at Kennedy Space Center: A four-year summary and evaluation

Since 1986, USAF forecasters at NASA-Kennedy have had available a surface wind convergence technique for use during periods of convective development. In Florida during the summer, most of the thunderstorm development is forced by boundary layer processes. The basic premise is that the life cycle of convection is reflected in the surface wind field beneath these storms. Therefore the monitoring of the local surface divergence and/or convergence fields can be used to determine timing, location, longevity, and the lightning hazards which accompany these thunderstorms. This study evaluates four years of monitoring thunderstorm development using surface wind convergence, particularly the average over the area. Cloud-to-ground (CG) lightning is related in time and space with surface convergence for 346 days during the summers of 1987 through 1990 over the expanded wind network at KSC. The relationships are subdivided according to low level wind flow and midlevel moisture patterns. Results show a one in three chance of CG lightning when a convergence event is identified. However, when there is no convergence, the chance of CG lightning is negligible

Weak positive cloud-to-ground flashes in Northeastern Colorado

The frequency distributions of the peak magnetic field associated with the first detected return stroke of positive and negative cloud-to-ground (CG) flashes were studied using lightning data from northeastern Colorado. These data were obtained during 1985 with a medium-to-high gain network of three direction finders (DF's). The median signal strength of positive flashes was almost two times that of the negatives for flashes within 300 km of the DF's, which have an inherent detection-threshold bias that tends to discriminate against weak signals. This bias increases with range, and affects the detection of positive and negative flashes in different ways, because of the differing character of their distributions. Positive flashes appear to have a large percentage of signals clustered around very weak values that are lost to the medium-to-high gain Colorado Detection System very quickly with increasing range. The resulting median for positive signals could thus appear to be much larger than the median for negative signals, which are more clustered around intermediate values. When only flashes very close to the DF's are considered, however, the two distributions have almost identical medians. The large percentage of weak positive signals detected close to the DF's has not been explored previously. They have been suggested to come from intracloud discharges and thus are improperly classified as CG flashes. Evidence in hand, points to their being real positive, albeit weak CG flashes. Whether or not they are real positive ground flashes, it is important to be aware of their presence in data from magnetic DF networks

Enhanced processing of 1-km spatial resolution fAPAR time series for sugarcane yield forecasting and monitoring

A processing of remotely-sensed Fraction of Absorbed Photosynthetically Active Radiation (fAPAR) time series at 1-km spatial resolution is established to estimate sugarcane yield over the state of São Paulo, Brazil. It includes selecting adequate time series according to the signal spatial purity, using thermal time instead of calendar time and smoothing temporally the irregularly sampled observations. A systematic construction of various metrics and their capacity to predict yield is explored to identify the best performance, and see how timely the yield forecast can be made. The resulting dataset not only reveals a strong spatio-temporal structure, but is also capable of detecting both absolute changes in biomass accumulation and changes in its inter-annual variability. Sugarcane yield can thus be estimated with a RMSE of 1.5 t/ha (or 2%) without taking into account the strong linear trend in yield increase witnessed in the past decade. Including the trend reduces the error to 0.6 t/ha, correctly predicting whether the yield in a given year is above or below the trend in 90% of cases. The methodological framework presented here could be applied beyond the specific case of sugarcane in São Paulo, namely to other crops in other agro-ecological landscapes, to enhance current systems for monitoring agriculture or forecasting yield using remote sensing.JRC.H.4-Monitoring Agricultural Resource

A Culturally Friendly Web-based Hybrid Intervention to Prevent Obesity: The SALSA Study

No Abstrac

MARS Bulletin Vol. 20, No. 11

BulletinJRC.H.4-Monitoring Agricultural Resource

MARS Bulletin Vol.21 No. 13

MARS Bulletin Vol.21 No. 13 CAMPAIGN REVIEWJRC.H.4-Monitoring Agricultural Resource

MARS Bulletin Vol.21 No. 11

MARS Bulletin Vol.21 No. 11JRC.H.4-Monitoring Agricultural Resource

MARS Bulletin Vol.21 No. 10

MARS Bulletin Vol.21 No. 10JRC.H.4-Monitoring Agricultural Resource