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The Danuri probe will use multiple scientific instruments to probe properties of the Moon.Credit: NASA

By this time next week, South Korea’s first lunar probe will be on its way to the Moon. The probe, Danuri, which means ‘enjoy the Moon’, should arrive at its destination by mid-December and orbit for a year.Researchers are eager for Danuri, which took more than six years to build and cost 237 billion won (US$180 million), to begin revealing insights about aspects of the Moon ranging from its ancient magnetism to ‘fairy castles’ of dust sprinkled across its surface. Researchers also hope that the craft, officially called the Korea Pathfinder Lunar Orbiter, will find hidden sources of water and ice in areas including the permanently cold, dark regions near the poles.Scientists in South Korea say the mission will pave the way for the country’s more ambitious plans to land on the Moon by 2030. Success for Danuri will secure future planetary exploration, says Kyeong-ja Kim, a planetary geoscientist at the Korea Institute of Geoscience and Mineral Resources in Daejeon, and principal investigator for one of Danuri’s instruments, a γ-ray spectrometer. “Everybody is so happy and excited,” says Kim, describing the lines of people who waved goodbye to the orbiter — safely packed in a container — on its way to the airport on 5 July.Danuri was flown from South Korea to the United States, and is now in Cape Canaveral, Florida, preparing to be placed on a Falcon 9 rocket that will take it beyond Earth’s orbit on 2 August.“The spacecraft is ready to launch,” says Eunhyeuk Kim, project scientist for the mission at the Korea Aerospace Research Institute (KARI) in Daejeon, but he still sometimes worries about whether the team is truly ready. “Until the time of the launch, we will be checking all the systems over and over and over.”Within an hour of launching, the 678-kilogram spacecraft will detach from the rocket and KARI will take control of it, extending the craft’s solar panels and deploying its parabolic antenna.“It’s just so cool to see more and more countries sending up their own orbiters and adding to the global understanding of what’s going on on the Moon,” says Rachel Klima, a planetary geologist at the Johns Hopkins University Applied Physics Laboratory in Laurel, Maryland, who is part of the science team.Fairy castlesDanuri will carry five scientific instruments. Among the most exciting is PolCam, which will be the first camera in lunar orbit to map the texture of the Moon’s surface using polarized light. Polarizers are popular for observations of Earth, such as those studying vegetation, but have not been sent to study the Moon, says Klima. By capturing how light reflects off the lunar surface, PolCam will be able to reveal characteristics such as the size and density of grains of dust and rock. This could help researchers to study unusual objects such as the tiny, porous towers of dust called fairy castle structures, says Klima. These structures can’t be reproduced on Earth because of its stronger gravity compared to the Moon, which makes them difficult to study.“It’s a ground-breaking instrument,” says William Farrand, a planetary geologist at the Space Science Institute in Boulder, Colorado, who will be working on PolCam data. Farrand hopes to use the data to study deposits of volcanic ash and improve understanding of the history of explosive eruptions on the Moon.Another widely anticipated instrument is ShadowCam, a highly sensitive camera provided by NASA that will take images of the permanently shadowed regions of the Moon, devoid of sunlight. The camera will need to rely on scattered light such as that from far-off stars to capture images of the surface topography.Since shortly after the Moon formed, volatile materials such as water from comets have been bouncing off its surface and becoming trapped in these very cold regions, says Klima. “We’ve got billions of years of Solar System history locked in the layers of these cold traps.” By giving researchers a view of the terrain in these regions, and identifying brighter regions that might be ice deposits, ShadowCam will be able to inform future landing missions to study that history, she says.MagnetismResearchers hope that data collected by Danuri’s magnetometer (KMAG) will help solve a mystery. The Moon’s surface displays highly magnetic regions; these suggest that for hundreds of millions of years in the Moon’s past, its core generated a magnetic field almost as powerful as Earth’s, through a process known as a dynamo, says Ian Garrick-Bethell, a planetary scientist at the University of California, Santa Cruz, who hopes to interpret KMAG data. But scientists are puzzled by how the Moon’s core, which is much smaller and proportionally farther from the surface than Earth’s, could have powered such an intense dynamo, and for so long. KMAG will take precise measurements of the Moon’s magnetic field to help them understand this.Garrick-Bethell hopes that towards the end of its life, the spacecraft will fly closer to the Moon to get even better measurements of the magnetic field. “The most exciting science would come if we flew closer to 20 kilometres.”The KARI team has not yet decided whether it will shrink Danuri’s orbit after the one-year mission is complete and eventually crash-land the craft on the Moon, says Eunhyeuk Kim. Alternatively, he says, the team could send the capsule into a higher orbit that could see it glide on for many more years. More

Occoquan Watershed Monitoring Laboratory, The Charles E. Via Jr Department of Civil and Environmental Engineering, Virginia Tech, Manassas, VA, USAShantanu V. Bhide, Stanley B. Grant, Emily A. Parker, Megan A. Rippy & Adil N. GodrejCenter for Coastal Studies, Virginia Tech, Blacksburg, VA, USAStanley B. Grant, Megan A. Rippy & Todd SchenkDepartment of Geology and Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USASujay KaushalFairfax Water, Fairfax, VA, USAGreg Prelewicz & Niffy SajiStormwater Planning Division, Public Works and Environmental Services, Fairfax, VA, USAShannon CurtisThe Charles E. Via Jr Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, VA, USAPeter Vikesland, Ayella Maile-Moskowitz, Marc Edwards & Kathryn G. LopezSchool of Public and International Affairs, North Carolina State University, Raleigh, NC, USAThomas A. BirklandUrban Affairs and Planning, School of Public and International Affairs, Virginia Tech, Blacksburg, VA, USATodd Schenk More

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Sociodemographic of respondentsA total of 140 households completed surveys with a response rate of 45.0%. The respondents were comprised of 48.6% males (n = 68) and 51.4% females (n = 72) in the general population aged 18 to 64 years, which were differentiated into five age groups: ≤19 (1.4%); 20–29 (22.1%); 30–50 (67.1%); 51–59 (5.0%); ≥60 (4.3%). There was a variation in terms of education levels and employment status; the majority of respondents were Bachelor-degree holders (at least 45%) and working as government servants (60.0%), as tabulated in Table 1. The accounted median monthly household income of Putrajaya is RM 7512 (~USD 1803, mean monthly household income of RM 10401, ~USD 2496), exceeding the national level (RM 4585, ~USD 1100)26. The survey covered household groups: bottom 40% (B40), middle 40% (M40), and top 20% (T20), classified into income groups ≤RM 2999, RM 3000–4999, RM 5000–6999, RM 7000–8999, RM 9000–10999, RM 11000–12999, and ≥RM 13000, where RM 1 approximately equivalent to USD 0.24 in average. On an average, respondents had lived in Putrajaya for seven years.Table 1 Descriptive statistics about risk perception of drinking water supply security with potential EDC contamination.Full size tableHuman morphology and drinking water consumption patternsThe present study involved 140 households with 257 total respondents (n = 257), consisting of infants (n = 4, aged less than 1 year; birth–5; 6–11 months), children (n = 77, aged 1 to 9 years; 1–3; 4–6; 7–9 years), adolescents (n = 37, aged 10 to 19 years; 10–14; 15–19 years), adults (n = 133, aged 20 to 59 years; 20–29; 30–50; 51–59 years) and elderly (n = 6, aged more than 60 years) (Table 2). Age groups were categorized based on previous studies27,28,29,30.Table 2 Age groups and respective mean body weight, body height, body mass index, daily water intake, and daily water intake per body weight.Full size tableThere were no significant differences between males (n = 125) and females (n = 132) in terms of body weight (t(235) = 1.671, p = 0.096), body height (t(225) = 0.804, p = 0.422), body mass index (t(246) = 1.116, p = 0.266), and daily water intake (t(255) = 0.483, p = 0.629). Surprisingly, males consumed more water than females in the United States and Australia19,31. Body weight showed a significant positive correlation to height based on Pearson product-moment correlation test (r = 0.861, p  More

Adjusting GEFS forecasts to local climatologyWhat amount of correction is required for GEFS forecasts to align with CHIRPS local climatology? The amount of correction varies widely across the globe and throughout the year. Figure 1a shows annual mean bias for GEFS reforecast 15-day totals. In this figure, wetter-than-CHIRPS climatology and systematic over-prediction of 15-day totals by GEFS is indicated by positive mean bias values, while the opposite is indicated by negative values. GEFS forecast mean bias was calculated for each month and then averaged across rainy season months, to focus aggregate results on the rainfall seasons, when precipitation forecasts are relevant. Monthly dry masks excluded locations with a monthly average of less than 10 mm, according to CHIRPS climatology. In general, one consistent result from Fig. 1a is a tendency to increase precipitation in many mountainous tropical and subtropical regions. By design, orographic precipitation enhancements in such regions are represented fairly well in CHIRPS, and these are carried through to CHIRPS-GEFS precipitation forecasts. The CHIRPS-GEFS bias-correction process reduces systematic errors (Fig. 1b), with the overall mean absolute bias error going from 24.1 mm for GEFS to 19.7 mm for CHIRPS-GEFS, an ~18% reduction.Fig. 1Annual mean bias and global error characteristics for GEFS reforecast data compared to CHIRPS, based on 15-day precipitation totals from Day 1, 6, 11, and 16 of each month during 2000–2019. Annual mean bias (a) shows the annual average of differences in GEFS reforecast and CHIRPS monthly means. Annual average error (b) shows the distribution of GEFS reforecast and CHIRPS-GEFS errors (product – CHIRPS). Both panels are based on in-season pixels, which are defined by monthly average CHIRPS  > 10 mm.Full size imageFigure 1a through Fig. 5 are based on GEFS reforecast, CHIRPS, and CHIRPS-GEFS data for the 5-day or 15-day periods beginning on the 1st, 6th, 11th, and 16th day of the month. All these exclude dry season months. Figure 1b shows the corresponding global distribution of annual average error for the GEFS reforecast and CHIRPS-GEFS, and is discussed later.GEFS has a large annual average positive bias of higher-than 40 mm in some areas of the globe, including in central Mexico, Central America, northern South America, the Andes and Himalayan Mountain ranges, and in southern China, Papua New Guinea, and localized areas of central Africa, the Ethiopian Highlands, and the western montane United States (Fig. 1a). GEFS has positive bias, by more than 5 mm for the annual average 15-day period, across the northern United States including in the Midwest, from Mexico’s northern mountains through most of Central America, in northern South America, the Andes range, eastern Brazil, in parts of central Europe, central and northern Asia, in the area from southern China to Myanmar and Thailand, and in northeastern and western India. GEFS has positive bias in portions of East Africa (Rwanda, Burundi, Tanzania, western Ethiopia), West Africa (Cameroon, Gabon), and Southern Africa (Zambia, central Angola, northern Zimbabwe, eastern South Africa). GEFS has negative bias, by more than 5 mm on average, in parts of central and northern Africa, Senegal, northern Australia, central South America, western India, the Yucatan peninsula, and the United States Gulf Coast.GEFS’ systematic bias changes throughout the year, as shown by the monthly mean bias in January, April, July, and October (Fig. 2). This is unsurprising, given that drivers of weather change too, but higher bias in particular months can be problematic for forecast users. In Ethiopia, for example, GEFS overestimates by large amounts during the Kirempt season (e.g., in July) and in October in the southwest. In central Brazil, the bias changes markedly by season, from a high negative bias in October to an expansive wet bias in April. In the Midwestern and northern United States, GEFS also shows a more expansive wet bias in April than in January, July, or October. In some areas, like in southern China and the Andes mountains, GEFS means are higher than CHIRPS means throughout the year.Fig. 2Monthly mean bias for GEFS reforecast data compared to CHIRPS, based on 15-day precipitation totals from Day 1, 6, 11, and 16 of each month during 2000–2019. Mean bias for January (a), April (b), July (c), and October (d) shows the difference in GEFS reforecast and CHIRPS monthly means. Shown for in-season pixels, which are defined by monthly average CHIRPS  > 10 mm.Full size imageThe CHIRPS-GEFS downscaling procedure corrects for systematic errors in GEFS forecasts that vary spatially and temporally. To assess the efficacy of the CHIRPS-GEFS approach, we began by calculating the per-pixel difference between GEFS and CHIRPS, and CHIRPS-GEFS and CHIRPS for 15-day periods. These were calculated for each month, for in-season pixels, and then averaged across the year. We then looked at the histogram of the resulting differences (Fig. 1b), to identify the distribution of annual average errors in these two products. CHIRPS-GEFS errors are shown as gray bars and GEFS errors are overlaid as hollow red bars. A desirable pattern is more small errors (higher bars close to 0 mm) and fewer large magnitude errors (lower bars at larger precipitation values). As shown in Fig. 1b, the bias-correction procedure has this effect, and results in CHIRPS-GEFS having overall lower errors for global rainy seasons compared to GEFS. GEFS 15-day errors more commonly involve over prediction of observed amounts than under prediction, as shown by the higher proportion of positive versus negative moderate to large positive errors. Part of this is due to the lower limit of under prediction being zero precipitation, while over prediction can range from marginal precipitation amounts to very high amounts. As shown in Fig. 1b, the CHIRPS-GEFS bias correction particularly reduces GEFS forecast errors for moderate-to-high rainfall amounts, and it results in a global 15-day error distribution that has a higher proportion of small errors, e.g., errors within −10 mm to 10 mm of CHIRPS values (51% for CHIRPS-GEFS and 43% for GEFS). Errors in categories ranging from 10 mm to 40 mm occur less often in CHIRPS-GEFS, globally, with probabilities in those categories reduced by around 15 and 25 percent at 10 mm to 20 mm and 20 mm to 30 mm, respectively, and by around 30 percent to 40 percent for errors that are higher than 40 mm.Next, we show performance of the 5-day and 15-day CHIRPS-GEFS precipitation forecasts by correlations and mean absolute errors for the historical record, compared to CHIRPS data for these periods. As described in Data Records, multiple outlets use forecast amounts for these periods. In the Usage Notes section, probability of detection scores for 15-day CHIRPS-GEFS in Africa are presented while describing an operational application of the CHIRPS-GEFS for seasonal monitoring. In that discussion we also examine the performance of 5-day forecasts during the 2020–2021 season in key regions of Kenya, Angola, Zambia, Zimbabwe, and Madagascar.Pearson correlation coefficients for 5-day and 15-day CHIRPS-GEFS, compared to CHIRPS (Fig. 3), indicate the ability of forecasts to predict deviations from average. It should be noted that correlations are nearly entirely driven by the information coming from the GEFS forecasts. The conversion to CHIRPS-GEFS adjusts the GEFS values to make them more “CHIRPS-like,” while also approximating the historical context of the GEFS forecast. Wet extremes forecasted by GEFS translate into wet extremes in CHIRPS-GEFS. Areas with very low correlations (R  0.7) are the United States, Western Europe, and Eastern Europe, southeastern South America, southern Central Asia, eastern China, parts of East and Southern Africa, and Australia. Globally, correlations are higher in January, April, and October than in July, which indicates generally higher forecast accuracy in those months. Exceptions are in eastern China, southern Brazil, eastern Mexico, northeastern Ethiopia, and central and southern Australia, where July correlations are not substantially lower. 15-day forecasts also have high correlations in some areas, including in the Western and Midwestern United States in January, in central and northern Australia in April, and in eastern Brazil in January and October.Fig. 3CHIRPS-GEFS 5-day and 15-day Pearson correlation coefficients, as compared to CHIRPS, for January, April, July, and October. (Validation data: CHIRPS 5-day and 15-day totals from the 1st, 6th, 11th, and 16th of the month, for 2000 to 2019. Shown for in-season pixels, which are defined by monthly average CHIRPS  > 10 mm.Full size imageIn Africa, a region where CHIRPS data is actively used by the Famine Early Warning System Network (FEWS NET) and other organizations for seasonal monitoring and drought early warning, forecast correlations indicate moderate to good 5-day and 15-day forecast performance in areas of East Africa, Southern Africa, and western North Africa during rainy season months. Some of the highest 15-day correlations in Africa are during important rainy season months, for example, in northeastern Ethiopia in July and April, in Kenya in April, in Zimbabwe and southern Mozambique in January, and in the Sudanian zone of West Africa in October. Very low correlations indicate low forecast skill in the Sahel, coastal West Africa, and in Central Africa in the DRC, Republic of the Congo, and Gabon.Mean absolute error of the bias-corrected GEFS forecasts highlight the areas where forecast amounts have historically been less reliable (Fig. 4). These indicate non-systematic errors associated with rains not materializing in the forecast location in the forecast period, which can be from GEFS model deficiencies and the inherent challenges of weather forecasting. Extreme precipitation events and warm season, deep moist convection-driven precipitation are notorious challenges for numerical weather prediction systems48,49, and CHIRPS-GEFS data are not immune to this problem. Remotely sensed data, including CHIRPS, also struggle with estimating extreme high rainfall amounts13,50, though since we are comparing CHIRPS-GEFS to CHIRPS, the main source of the large errors shown here would be the GEFS reforecast.Fig. 4CHIRPS-GEFS 5-day and 15-day mean absolute errors, as compared to CHIRPS, for January, April, July, and October. Validation data: CHIRPS 5-day and 15-day totals from the 1st, 6th, 11th, and 16th of the month, for 2000 to 2019. Shown for in-season pixels, which are defined by monthly average CHIRPS  > 10 mm.Full size imageAs shown in Fig. 4, the magnitude of errors follows climatology, with 5-day errors typically under 10 mm for drier rainy season months. In wetter months and locations errors are typically between 10 mm and 20 mm. With higher rainfall magnitude there is greater potential for larger errors. The 15-day forecast errors exhibit a similar spatial pattern to the 5-day errors, and error magnitudes correspond to the three-times larger accumulation interval as well as expected lower skill at longer lead time. Figure 4 shows especially large 15-day mean absolute errors in January near northern Mozambique and Madagascar, in July and October in parts of Central America, in April in central Kenya and southwestern Tanzania, in July in India’s Western Ghats Mountains and in the Himalayas, and in the Maritime Continent. In southeast China, while the 15-day correlations indicated decent skill at forecasting the sign of precipitation anomalies, large 15-day errors indicate the influence of poorly forecast large storms, which unbiasing cannot correct for. In the Amazon rainforest, many areas with low correlations also have high forecast errors, underscoring poor forecast performance there. More