Lake-effect rains over Lake Victoria and their association with Mesoscale Convective Systems

Nicholson, Sharon E.; Klotter, Douglas; Hartman, Adam T.

doi:10.1175/jhm-d-20-0244.1

Cited by 8 publications

(5 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lastly, Case 5 revealed that the atmospheric instability (PW > 45 mm, CAPE < 900 J/kg; K-index > 35 • C and TTs < 45 (Figure 15c-f, red box)) was favorable for convective activity on 27 August 2020 and this is consistent with the guidelines for heavy downpours [71]. Additionally, this result is consistent with the climatological behavior of the region as described by Nicholson [78][79][80]. In Case 3, the synoptic conditions on this day over the range 25-35°E and 5-10°N (red box) showed unique characteristics.…”

Section: Synoptic Conditions During Heavy Pre Eventssupporting

confidence: 86%

Assessing the Performance of WRF Model in Simulating Heavy Precipitation Events over East Africa Using Satellite-Based Precipitation Product

Nooni

Tan

Hongming³

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

This study investigated the capability of the Weather Research and Forecasting (WRF) model to simulate seven different heavy precipitation (PRE) events that occurred across East Africa in the summer of 2020. The WRF model outputs were evaluated against high-resolution satellite-based observations, which were obtained from prior evaluations of several satellite observations with 30 stations’ data. The synoptic conditions accompanying the events were also investigated to determine the conditions that are conducive to heavy PRE. The verification of the WRF output was carried out using the area-related root mean square error (RMSE)-based fuzzy method. This method quantifies the similarity of PRE intensity distribution between forecast and observation at different spatial scales. The results showed that the WRF model reproduced the heavy PRE with PRE magnitudes ranging from 6 to >30 mm/day. The spatial pattern from the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification-Climate Data Record (PERSIANN-CCS-CDR) was close to that of the WRF output. The area-related RMSE with respect to observation showed that the error in the model tended to reduce as the spatial scale increased for all the events. The WRF and high-resolution satellite data had an obvious advantage when validating the heavy PRE events in 2020. This study demonstrated that WRF may be used for forecasting heavy PRE events over East Africa when high resolutions and subsequent simulation setups are used.

show abstract

Section: Synoptic Conditions During Heavy Pre Eventssupporting

confidence: 86%

Assessing the Performance of WRF Model in Simulating Heavy Precipitation Events over East Africa Using Satellite-Based Precipitation Product

Nooni

Tan

Hongming³

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…We note that Nicholson et al. (2021) argued that IMERG‐Final tends to overestimate the precipitation over inland water bodies and attributed the likely causes to the passive microwave assessments of strong convection. The ensemble average of the satellite‐based estimations exhibits that the averaged summertime surface precipitation rate over Lake Baikal is 1.95 mm d −1 , which is 81% of that in the domain.…”

Section: Resultsmentioning

confidence: 71%

“…The majority of the satellite-based estimations (PERSIANN-CDR, GSMaP, and MSWEP) and the ERA5 reanalysis reveal that the summertime precipitation amount over Lake Baikal is smaller than that in the surrounding land area and that this reduction is more pronounced in the northern part of the lake. We note that Nicholson et al (2021) argued that IMERG-Final tends to overestimate the precipitation over inland water bodies and attributed the likely causes to the passive microwave assessments of strong convection. The ensemble average of the satellite-based estimations exhibits that the averaged summertime surface precipitation rate over Lake Baikal is 1.95 mm d −1 , which is 81% of that in the domain.…”

Section: Resultsmentioning

confidence: 88%

Effects of Lake Baikal on Summertime Precipitation Climatology Over the Lake Surface

Lee,

Ganbat,

Jin

et al. 2023

Geophysical Research Letters

View full text Add to dashboard Cite

Lakes reduce temporal variations in near-surface air temperature because the heat capacity of water is larger than that of the surrounding land and can increase the amount of water vapor in the atmosphere above them through active evaporation from their surface. Owing to the thermal and humidity contrasts between a lake and its surrounding area, a lake can be anticipated to modulate weather and impact climate around it (Camberlin et al., 2018;Laird et al., 2009). Lake-induced changes in precipitation vary depending on factors such as season and geographical location of a lake. Since the atmosphere over the lake is generally warmer and more humid than that over the surrounding land in winter unless the lake is ice-covered, convection over the lake becomes more active and snowfalls prevail on the downwind side of the lake. This is referred to as lake-effect snow, readily observed for many lakes (e.g., Peace & Sykes, 1966;Steenburgh et al., 2000). In summer, on the other hand, the cool lake surface stabilizes the lower atmosphere over it, which reduces convective clouds and precipitation over the lake and its downwind area (e.g., Gu et al., 2016;Lyons, 1966). Such seasonally reversed lake effects on precipitation have been reported for midlatitude lakes such as the Great Lakes in North America (Notaro et al., 2013) and Lake Ladoga in Europe (Samuelsson et al., 2010). However, the African Great Lakes in the tropics, where neither warm nor cold seasons exist, exhibit substantial lake-induced increases in over-lake precipitation during wet seasons. This is attributed to the lake surface having ∼2.5 times greater moisture flux than the moisture flux from the equivalent land surface (Thiery et al., 2015). For lakes located in basins such as Lake Victoria, the terrain surrounding the lakes plays

show abstract

“…A similar spatial pattern is apparent in the higher resolution satellite products but is not evident in any of the reanalysis products. In a more detailed evaluation, Nicholson et al [5] concluded that TRMM 3B43 provided the best estimate over the lake and gave values similar to that which is shown in Figure 8. Figure 8 shows mean rainfall over the lake, based on a combination of gauge records from island stations and dynamic modeling.…”

Section: Mean Rainfallmentioning

confidence: 65%

“…This holds true for reanalysis products as well [4]. The second problem is that the products which are strongly dependent on passive microwave data produce questionable results over the extensive lakes of eastern Africa [5]. The purpose of this article is to demonstrate the extent of these problems, to determine the most reliable products in these two regions, and to suggest some possible explanations and solutions for the diversity of the estimates.…”

Section: Introductionmentioning

confidence: 97%

Assessing the Reliability of Satellite and Reanalysis Estimates of Rainfall in Equatorial Africa

Nicholson

Klotter

2021

Remote Sensing

Self Cite

View full text Add to dashboard Cite

This article examines the reliability of satellite and reanalysis estimates of rainfall in the Congo Basin and over Lake Victoria and its catchment. Nine satellite products and five reanalysis products are considered. They are assessed by way of inter-comparison and by comparison with observational data sets. The three locations considered include a region with little observational gauge data (the Congo), a region with extensive gauge data (Lake Victoria catchment), and an inland water body. Several important results emerge: for one, the diversity of estimates is generally very large, except for the Lake Victoria catchment. Reanalysis products show little relationship with observed rainfall or with the satellite estimates, and thus should not be used to assess rainfall in these regions. Most of the products either overestimate or underestimate rainfall over the lake. The diversity of estimates makes it difficult to assess the factors governing the interannual variability of rainfall in these regions. This is shown by way of correlation with sea-surface temperatures, particularly with the Niño 3.4 temperatures and with the Dipole Mode Index over the Indian Ocean. Some guidance is given as to the best products to utilize. Overall, any user must establish that the is product reliable in the region studied.

show abstract

Lake-effect rains over Lake Victoria and their association with Mesoscale Convective Systems

Cited by 8 publications

References 66 publications

Assessing the Performance of WRF Model in Simulating Heavy Precipitation Events over East Africa Using Satellite-Based Precipitation Product

Assessing the Performance of WRF Model in Simulating Heavy Precipitation Events over East Africa Using Satellite-Based Precipitation Product

Effects of Lake Baikal on Summertime Precipitation Climatology Over the Lake Surface

Assessing the Reliability of Satellite and Reanalysis Estimates of Rainfall in Equatorial Africa

Contact Info

Product

Resources

About